Special Edition on Advanced Analytics in Banking

McKinsey on Payments Special Edition on Advanced Analytics in Banking

Highlights

4 13 36

The analytics-enabled How machine learning can Hidden figures: The quiet collections model improve pricing performance discipline of managing people using data

52 60 68

Designing a data Building an effective “All in the mind”: Harnessing transformation that delivers analytics organization psychology and analytics to value right from the start counter bias and reduce risk

Volume 11 Number 28 August 2018 McKinsey on Payments is written by experts and practitioners in the McKinsey & Company Global Payments Practice.

To send comments or request copies, email us at: [email protected]

To download selected articles from previous issues, visit: https://www.mckinsey.com/industries/financial-services/our-insights/payments

Editorial board for special analytics issue: Kevin Buehler, Vijay D’Silva, Matt Fitzpatrick, Arvind Govindarajan, Megha Kansal, Gloria Macias-Lizaso Miranda

McKinsey on Payments editorial board: Alessio Botta, Philip Bruno, Robert Byrne, Olivier Denecker, Vijay D’Silva, Tobias Lundberg, Marc Niederkorn

Editors: John Crofoot, Peter Jacobs, Glen Sarvady, Anne Battle Schultz, Jill Willder

Global Payments Practice manager: Natasha Karr

Global Advanced Analytics in Banking Practice manager: Josephine Axtman

Managing editor: Pa ul Feldman

Design and layout: Derick Hudspith

This publication is not intended to be used as the basis for any transaction. Nothing herein shall be construed as legal, financial, accounting, investment, or other type of professional advice. If any such advice is required, the services of appropriate advisers should be sought. No part of this publication may be copied or redistributed in any form without the prior written permission of McKinsey & Company. Contents

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The analytics-enabled How machine learning can Combating payments fraud collections model improve pricing and enhancing customer performance experience

28 36 44

Using data to unlock the Hidden figures: The quiet Using analytics to increase potential of an SME and discipline of managing satisfaction, efficiency, and mid-corporate franchise people using data revenue in customer service

52 60 68

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Mapping AI techniques to Data sheet: Advanced problem types analytics Foreword

In this special edition of McKinsey on Payments , we examine the emergence of ad - vanced analytics as one of the biggest disruptions in financial services, influenc - ing almost every part of the industry. A recent McKinsey Global Institute study estimated the annual potential value of artificial intelligence in banking at as much as 2.5 to 5.2 percent of revenues, or $200 billion to $300 billion annually, based on a detailed look at over four hundred use cases.

The importance of data and analytics in banking is not new. The 1950s and 1960s saw innovations such as credit scoring in consumer credit, and the use of market data for securities trading, driven by the desire for more data-driven decisioning. The 1970s and 1980s unleashed direct marketing of credit cards (Citibank’s “drop” of pre-approved credit cards to 20 million Americans in 1977 instantly made it one of the largest US issuers) and the emergence of new financial deriva - tives and risk-transfer vehicles, enabled by the growth of mainframe computers. The 1990s and 2000s saw the institutionalization of data and analytics, with the launch of the FICO score in 1989 by Fair, Isaac and Co, and the introduction in 2004 of Basel II, which embedded a data-driven analytical framework into bank regulation. This period, during which computing and storage power skyrocketed, also featured the emergence of nimble credit card players in the US such as Capi - tal One, Providian, MBNA, and First USA, and of hundreds of quant-driven hedge funds globally in wholesale banking.

But it is this decade’s explosion of available data and ubiquitous channels that is starting to fundamentally change the industry. It was once estimated that five ex - abytes of data were created from the dawn of civilization through 2003. By 2016, the world was producing two exabytes an hour, a pace that is still increasing. It is therefore no surprise that banks and payments firms globally are struggling to leverage the new data available, while hampered by legacy infrastructure, out - moded organizational structures and skills, and the limitations of regulatory guardrails.

To truly understand how some banks are starting to change, it helps to look at four distinct pillars:

1. Organizing for use cases. Some banks believe in starting by building the right infrastructure. Unfortunately, “build-it-and-they-will-come” strategies rarely succeed. While there is no available estimate of wasted spend, we do know that 70 percent of large projects fail overall. Far more effective is a “cus - tomer-back” use case-driven approach, often clustered into domains. For banking, we have identified hundreds of cases of analytics-driven impact, which range from using simple “random forest” algorithms to predict call traf -

1 McKinsey Analytics

McKinsey Analytics helps clients achieve better performance through data, working with more than 2,000 clients to build analytics-driven organizations, and help them develop the data strategies, operations, and analytical capabilities to deliver sustained impact. In the last few years, McKinsey Analytics has grown rapidly, acquiring seven specialty analytics firms and partnering with more than 150 other providers. Today, McKinsey An - alytics brings together over 2,000 advanced analytics and AI experts and spans more than 125 domains (industry- and function-specific teams with people, data, and tools focused on unique applications of analytics), working with client, external, and McKinsey proprietary data in a secure environment. QuantumBlack, an advanced analytics firm that McKinsey acquired in 2015, is now a core part of McKinsey’s research, develop - ment, and delivery of AI techniques and real-world applications for clients. Learn more at www.mckinsey.com/business-functions/mckinsey-analytics/our-insights and https://www.quantumblack.com/

fic, to leveraging natural language processing to scan résumés (as McKinsey does) or contracts, to taking advantage of deep learning and image processing to detect fraud. Mapping out opportunities and their business cases is the right first step, along with the front-line adoption necessary to realize value.

2. Building a useable data and analytics infrastructure. Ubiquitous data does not always translate to useable information, though most banks have a treasure trove of data that they could be using today. Many institutions are still early in the process of defining their data infrastructure, and the risk of bad data and models cannot be overstated. Based on which use cases have the greatest priority, issues that banks are sorting out are (1) the economic, serv - ice, and regulatory trade-offs between public cloud, private cloud, and other on-premise solutions, (2) challenges in defining and enforcing an effective data model which requires front-line and business commitment and shared in - vestments, and (3) how to build an effective chief data officer (CDO) function with real teeth to manage the process and maintain effective quality control.

3. Developing a standardized analytical environment. There is a prolifera - tion of analytical toolkits today. Some firms are literally “boiling the ocean” of available data to gain insight. For instance, McKinsey’s automated analytical engines can now test millions of permutations in minutes, incorporating ex - ternal data sources like market information, census data, the weather, and mo - bile location data. Mature banks are now focusing on how best to balance

2 toolkit innovation with standardization to move from “one offs” to enabling analytics at scale. Agile approaches are particularly effective, especially when small differences can mean big money—for instance, when response rates to digital marketing campaigns are less than five basis points, the slightest edge is valuable.

4. Building an effective organization. Banks are becoming more thoughtful about the organizational infrastructure and people to support a data and ana - lytics transformation. These are some of the most in-demand roles today and firms are using a combination of build versus rent models to accelerate execu - tion. Universities are trying to fill the gap but not fast enough—last year, MIT’s Masters in Business Analytics program received the highest application rate per open seat of any master’s program at the institute. Within banks, the de - bate is about how best to organize and deploy these resources—ranging from centers of competence to distributed groups closer to the business. When banks do set up central analytics groups, these average 2.8 percent of the total employee base—nearly triple the percentage for other industries, according to McKinsey’s Analytics Quotient tool.

For this issue, we have selected a set of complementary articles to exemplify both the opportunities and challenges faced by financial firms. We start with six inno - vative use cases demonstrating the potential of next-generation analytics to serve as a differentiator in collections, pricing, fraud detection, segment revenue growth, talent management, and customer service. We then pivot to three foun - dational articles on designing a data transformation for value creation, building an effective analytics organization, and detecting and reducing bias in implemen - tation. We end the issue with a primer on mapping AI techniques to problem- types, and our customary data pages.

We hope you find this issue thought-provoking and that these articles generate constructive conversations. As always, we welcome your feedback at [email protected] .

Vijay D’Silva is a senior partner in McKinsey’s New York office.

3 The analytics-enabled collections model

The global credit environment absorbed the effects of the financial crisis at varying speeds from market to market. In some places, loss rates have remained relatively high since 2008–09; in others, the past decade has been one of steady improvement, with tapering losses that have only recently begun to climb again. In the expanding markets, lenders increased their risk exposure, issuing new products designed around easier underwriting guidelines. Little attention was paid to maintaining or improving collections capabilities. As debt loads rise, however, institutions in these markets are beginning to rebuild collections staff and skills that eroded in the previous period. Meanwhile, in the more stressed markets, the need for more efficient and effective collections operations is likewise becoming a priority.

Ignacio Crespo The need to renew collections operations pro - Some of the most significant advances Arvind Govindarajan vides lenders with an ideal occasion to build brought about by advanced analytics and in new technologies and approaches that were machine learning are in customer segmenta - unavailable when the financial crisis hit. The tion, which is becoming much more sophisti - most important advances in collections are cated and productive. Better being enabled by advanced analytics and ma - segmentation—including innovative behav - chine learning. These powerful digital innova - ioral segmentation, discussed in detail in tions are transforming collections operations, “‘All in the mind’: Harnessing psychology helping to improve performance at a lower and analytics to counter bias and reduce cost. Better criteria for customer segmenta - risk,” on page 68—is providing the basis for tion and more effective contact strategies are more effective collections processes and being developed. Individual collector per - strategy. The improvements affect the com - formance is being improved with better plete collections agenda, beginning with the credit-management information and other prevention and management of bad debt and tools. Contact can be managed through an extending through to internal and external array of channels, some allowing customers a account resolution. greater sense of control over their finances. A next-generation collections model Loss-forecasting strategies can also be made In traditional collections processes, banks seg - more accurate and predelinquency outreach regate customers into a few simple risk cate - made more effective with enhanced financial gories, based either on delinquency buckets or tools and mobile apps .

Editor's note: This article first appeared in McKinsey on Risk in June 2018. Since then, delinquencies across many asset classes have continued to rise, and the importance of analytics in collections has only increased.

The analytics-enabled collections model 4 on simple analytics, and assign customer-ser - By using advanced analytics and applying ma - vice teams accordingly. Low-risk customers are chine-learning algorithms, banks can move to usually given to newer collections agents based a deeper, more nuanced understanding of on availability; the agents follow standardized their at-risk customers. With this more com - scripts without being asked to evaluate cus - plex picture, customers can be classified into tomer behavior. Agents with moderate experi - microsegments and more targeted—and ef - ence, training, and skills are assigned, again fective—interventions can be designed for based on availability, to medium-risk cus - them (Exhibit 1). tomers. These agents also follow a standard - Using analytics in the new model ized script but are trained to assess customer Analytics-based customer segmentation is at behavior based on ability and willingness to the center of the next-generation collections pay. High-risk customers are assigned to the model. The transformed collections model most skilled agents, who own their accounts will allow lenders to move away from decision and use less standardized approaches to de - making based on static classifications, velop assessments of customer behavior. Con - whether these are standard delinquency tact strategies and treatment offerings are stages or simple risk scores. Early identifica - fairly varied across the risk categories. tion of self-cure customers will be one bene -

Exhibit 1 Advanced analytics and machine learning can classify customers into microsegments for more targeted interventions.

Customer True low-risk Absent-minded Dialer-based True high-touch Unable to cure type

Targeted Use least Ignore or use Matching agents Focus on Offer debt intervention experienced interactive voice to customers customers able restructuring agents provided message and live prompts to pay and at settlements with set scripts. (segment will to agents to high risk of not early for those probably modify scripts. paying. truly underwater. self-cure).

Impact Agent−client 10% time Can lead to Added focus SigniÞcant conversation savings allows increased addresses increase in guided by agents to be “connection” higher restructuring onscreen reassigned to and higher probability of and settlements prompts more difÞcult likelihood default rates in enhances based on customers and of paying. this segment. chance of probability speciÞc collecting at of breaking campaigns. least part of promises. debt.

Source: McKinsey analysis

5 McKinsey on Payments August 2018 fit. Another will be an approach based on dress certain borrowers (such as recurring de - value at risk, rather than blanket decisions faulting clients) with effective initiatives. based on standardized criteria. The aspira - Other groups of clients were identified, and tion is to have every customer as a “segment exit strategies based on economic value were of one” with customized treatments. developed for each group. The results are com - pelling. The bank reduced its 90-day-or-more portfolio by more than €100 million, with €50 Most banks can achieve significant million in fewer past-due entries and the re - results in all key collections areas by mainder in exit acceleration. Moreover, a re - duction of 10 percent in past-due volumes was introducing an analytics-based solution achieved across the board, worth around €300 quickly and then making needed million less in past-due exposure. improvements as they go. A leading North American bank has rolled out a number of machine-learning models that im - prove the estimation of customer risk, identify - Leaders taking the analytics-based actions ing customers with a high propensity to that define the new model have already begun self-cure as well as those suitable for early of - to realize gains in efficiency and effective - fers. These models have so far enabled the bank ness. One European bank automated 90 per - to save $25 million on a $1 billion portfolio. cent of communications with clients by Most banks can achieve results of this magni - developing two advanced-analytics models tude by introducing an analytics-based solu - using machine-learning algorithms. A binary tion quickly and then making needed model identifies self-curers and non-self-cur - improvements as they go. Value can be gained ers, and a multiclass model recommends col - in almost all of the key areas in the collec - lections strategies for the non-self-curers, tions environment. including soft measures, restructuring, or work outs. The models use around 800 vari - • Early self-cure identification. Some ables, including client demographics and in - banks use rudimentary heuristics (rules of formation on overdrafts, client transactions, thumb) or simple models to identify self- contracts, and collaterals. The bank has real - cure customers, while others have adopted ized more than 30 percent in savings with no simple self-cure models with limited vari - loss in operational performance. ables. The new self-cure model based on machine learning and big data can save col - Another European bank set out to develop a lectors a lot of time. By using many vari - top-notch recovery process using advanced ables to better identify self-cure accounts, analytics. The goals were to minimize the banks can increase collector capacity by 5 number of clients falling 90 or more days’ past to 10 percent, allowing agents to be reas - due while maximizing the economic impact of signed to more complex collections cases. exits, focusing on retail and small-and- medium enterprise portfolios. As the bank • Value-at-risk assessment. While many gained a deeper understanding of its nonper - banks use time in delinquency as the pri - forming loans, it was able immediately to ad - mary measure of default risk, some lenders

The analytics-enabled collections model 6 are taking a more sophisticated approach, an approach that increases the uptake of building a risk model to determine value at offers while reducing charge-offs by 10 to risk. Many of these are simple trees and lo - 20 percent. gistic regressions, however, with limited • Optimizing pre-charge-off offers. Banks data. Leaders are moving to a future state are currently using rules or simple analyt - in which models project conditional proba - ics to create offers for customers, often bility rather than assign customers single without determining the likelihood that risk scores. The conditional score is de - they will accept. Models will predict the pendent on a range of tailored approaches best offer, optimized for the needs of the to customer contact and engagement: bank and the customer. Banks can change every borrower has several scores depend - the prompt, adjusting loan characteristics ing on the contact strategy and offer. and offerings to those most likely to reduce Lenders would then use the strategy and charge- offs, including reamortizing the offer that optimizes recoveries. The ap - term or interest rate, consolidating loans, proach better calibrates the intensity of or settling. Making the right offer early, be - contact with each account, thus optimizing fore accounts enter late-stage delinquency, resources. A next-generation value-at-risk can improve acceptance rates. assessment can further reduce charge-offs by 5 to 15 percent depending on maturity • Post-charge-off decision. Most banks use of current operations, analytics, and avail - simple models or heuristics to determine ability of data. which agencies to send accounts to and at what price. To refine these decisions, models Lenders at the analytics will determine the best agency for each ac - count and tailor prices accordingly. The forefront are assembling masses of model will also determine the optimal pric - data from many kinds of sources and ing segmentation for third-party agencies and identify the accounts to retain in-house developing different models to serve longer (based on products retained with the collections goals. bank, for example). The strategic use of third parties can help with accounts that cannot be cured internally. • Cure assessment versus pre-charge- off offers. At most banks, agents deter - Integrated analytics models mine whether a customer will cure or will Lenders at the forefront of the analytics need an offer of some sort; some banks transformation are assembling masses of data have heuristic rules for agents to follow. from many kinds of sources and developing The new approach is to use models that different models to serve collections goals. ascertain a customer’s ability and willing - The data sources can include customer demo - ness to pay and gauge whether the better graphics, collections and account activity, and path is a cure or an offer. Banks can reseg - risk ratings. The most sophisticated lenders ment delinquent accounts to improve are creating “synthetic” variables from the their decisions to offer early settlement, raw data to further enrich their data. Ma -

7 McKinsey on Payments August 2018 chine learning helps identify markers for count, deployed the solution inside the exist - high-risk accounts from such variables as ing collections work-flow environment, and cash-flow status, ownership of banking prod - trained collectors to use the system and col - ucts, collections history, and banking and in - lect additional data to improve model per - vestment balances. By using so many inputs formance. The initiatives were up and from many different systems, lenders can running in about four months (Exhibit 2). dramatically improve model accuracy, lower Contact strategies and treatment charge-off losses, and increase recovered approaches amounts. Two separate institutions recently Institutions adopting the most analytics-for - adopted similar approaches using more than ward approaches have been intensifying the 100 variables to support numerous machine- development of new treatment and contact learning models. These issuers used machine strategies, expanding the limits of digital ca - learning to identify the optimal treatment pabilities. By applying advanced analytics and and contact strategy for each delinquent ac - machine learning, banks can identify the

Exhibit 2 Two major issuers used machine learning and more than 100 variables to accelerate development of treatment and contact strategies.

Integrated data sources on Multiple machine-learning Implemented in the existing client behavior models used to identify collections environment features of high-risk accounts

Customer demographics Low cash ßow Contact-center interface

Banking machine Collections activity More banking products (automated touchpoint)

Contact customer via Account activity Previously in collections call or text (automated touchpoint)

Payments High total balance Interactive voice across products response (automated touchpoint)

Risk ratings Low investment balance

• 140 inputs across 15 systems • 30–40% improvement in • Implemented in 12–16 describe proÞles of each client model accuracy versus weeks on each day in collections previously existing models

Impact: 4−5% lower net charge-off losses

Source: McKinsey analysis

The analytics-enabled collections model 8 most promising contact channels while also optimal contact sequencing can increase suc - developing digital channels to define innova - cess in early stages of delinquency. tive and regulatory-compliant contact strate - The analytics focus on the front line gies. The same digital channels can be used to Leading companies in many sectors—digital build awareness of payment options. giants, healthcare providers, retailers, and • Websites. Display messages and repay - manufacturers— are using data and analytics ment options as soon as customers log in, to develop a workforce optimized to business increasing awareness and providing oppor - goals. Analytics is now the source of im - tunities for early delinquency reduction. proved performance in realizing talent strategies as well as a means for linking talent • Messenger and chat. Where legally per - strategy to business needs. (See “Hidden fig - missible, collectors can contact customers ures: The quiet discipline of managing people and negotiate payment options with chat using data”, page 36.) Presently, recruiting functionality and free messenger applica - and retention are often based on legacy tions (such as WhatsApp). processes, including résumé screening and • Mobile apps. Build collections functional - interviews; retention is based solely on per - ity into the mobile app, reminding cus - formance. Analytics can improve hiring, find - tomers in early delinquency stages to pay ing agents with affinities to the most valuable and offering payment options. at-risk segments, as well as help identify col - • Virtual agent. Create capacity by devel - lectors at risk of leaving. Companies are using oping virtual agent functionality to call machine-learning algorithms to screen ré - customers in early delinquency stages. sumés and to determine the value of external hiring compared with internal promotion. • Voice-response unit. Enhance current One global digital company used analytics to voice-response capability, offering basic create a checklist that boosted onboarding repayment options when customers call, speed by 15 percent. The algorithms, it should which frees collector capacity. be stressed, are not replacing human judg - Most banks use heuristics to establish the ment but are rather providing a deeper fact best times to call. Usually, however, agents base for the exercise of informed judgment. are inadequately supported on questions of Companies are also using algorithms to un - which channel to use, when to use it, and cover the bottom-line impact of employee en - what the message should be. Advanced mod - gagement and to drive deeper engagement els can project a full channel strategy, includ - across the organization. In collections, where ing channel usage, timing, and messaging. retention of talent is a recurring issue, people Banks will be able to control contact down to analytics can be used to find the drivers of per - the hour and minute, as well as the sequence formance, including personality profiles and of communications—including voice, text, risk factors for low performance and engage - email, letter, and interactive voice message. ment. By identifying individuals most at risk of The approach is developed to maximize the leaving, for example, banks can take responsive right-party-contact rate and influence cus - measures to optimize their talent pool for sus - tomer behavior to prioritize payment. Such tained performance improvement.

9 McKinsey on Payments August 2018 Machine learning and nontraditional data giving agents more prescriptive decision sup - have become the new frontier in collections- port in certain situations, including a wider decision support. Audio analytics, for exam - range of set script elements and narrower pa - ple, is now an important tool for rameters for negotiations, banks can free ca - understanding front-line effectiveness. By al - pacity and redirect resources toward the lowing algorithms to work through thousands most valuable accounts. In this vein, one card of conversations, banks can discover the most issuer achieved dramatic improvements in productive and engaging approaches. With the rate of promises kept in its high-risk seg - hypotheses informed by insights from the ment by using an approach enabled by data field of behavioral science, banks are also and analytics to script elements, including using machine learning to diagnose and neu - behavioral insights (Exhibit 3). tralize the biases that affect collector and Behavioral pairing and agent coaching customer decision making. At the same time, Many banks do not apply agent–customer the machine-learning approach is enabling pairing uniformly or deliberately. When it is automation of larger classes of decisions. By

Exhibit 3 One card issuer systematically identiﬁed and implemented “assertive” script elements, doubling the promise-kept rate to 95 percent.

Diagnostic and Validation Implementation hypothesis generation

Hypotheses on psychological levers Impact of new psychological Levers embedded in a new that could increase promise-kept levers tested with 200 credit script for a high-risk segment rate derived from call shadowing, card collection calls with a low promise-kept rate collector debrieÞng, and customer Top levers selected for roll-out Ongoing coaching of collectors interviews Eight hypotheses identiÞed for testing

Examples of leversIncrease in promise-kept rate Overall promise-kept rate, % high-risk segment %

95 Anchor negotiation in full amount 8

x 2.2 Solve for ease of payment method 14

44 Generate “implementation intention” 15

Mention emotionally relevant 22 consequence No levers All levers

Source: McKinsey analysis

The analytics-enabled collections model 10 applied, high-risk customers are usually continuous improvement. Without rapid given to experienced, high-performing collec - iteration and deployment of models, value tors, while low-risk customers are assigned to is left unrealized. new collectors. Analytics-aided pairing helps • “Given compliance and regulatory issues, match collectors and customers who have models are too opaque to use.” Banks can se - similar personal profiles. By smarter pair - lect among a range of modeling techniques ing—matching delinquent clients with the with different levels of transparency. They agent expected to be most effective—out - can balance demands for transparency and comes can be improved and call times re - performance by choosing the most appro - duced. As for coaching, this has often priate algorithms. occurred in training sessions, huddles, and call monitoring by managers. Analytics-aided • “Success depends on nontraditional data.” coaching permits real-time feedback and For most collections applications, banks’ analysis in live phone calls. internal data can provide the majority of the gains from advanced analytics. Banks Breaking through artificial barriers to can begin by utilizing all internal data and transformation supplement with external data subse - Most banks understand that analytics and quently as needed. digital automation will transform their col - lections operations. Some have been reluc - • “Regulations and compliance negate many tant to get started, however, due to the of the benefits of advanced analytics and following persisting myths about the new machine learning.” A number of banks in technologies. highly regulated jurisdictions have already successfully deployed machine learning. • “Sophisticated data infrastructure is a pre - Indeed, machine learning can improve requisite.” While this is an advantage, it can compliance by better matching the right be developed over time. The truth is that treatment with the right customer and banks can build value-enhancing collec - avoiding biases. tions models with available data. As the data are improved, the models can be up - None of these myths should prevent banks dated accordingly. from beginning the analytics-enabled trans - formation of their collections operations. • “Both the collections front line and the digital There is no perfect way to start a transforma - infrastructure need to be in place before ana - tion—some of the implementation might lytics models can be implemented.” Actually, even be messy at first. The essentials of the models can be implemented using legacy in - analytics transformation in collections are frastructure, and the value they generate clear, however. First, set a long-term vision can be used to invest in the needed infra - but also a path toward it that generates value structure improvements. continuously. Second, work in an agile man - • “The development and implementation of ner, with teams from all dimensions of the models take a long time.” Banks can get transformation. Focus on implementing started using agile model development working models from day one, avoiding an with minimum viable products subject to overly complex academic approach. Use syn -

11 McKinsey on Payments August 2018 thetic variables to enhance model perform - quency challenges that market analysts pre - ance, and continuously experiment with dict are on the horizon. The transformation strategies to generate additional data for the of collections has in fact already begun, as next generation of models. leading institutions assemble the data and develop algorithms to attain improvements * * * in their existing collections context within a The next-generation collections environment few months’ time. These leaders are showing will be built around advanced analytics and the way by applying the new approaches and machine learning. These approaches will help making improvements as they go. And they lending institutions meet the new delin - are already generating bottom-line results.

Ignacio Crespo is a partner in McKinsey’s Madrid office, and Arvind Govindarajan is a partner in the Boston office.

In the next issue of McKinsey on Payments , we will explore how increasing digitization and advanced analytics are reshaping the collections discipline in ways that significantly improve overall effectiveness, efficiency, and the customer experience.

The analytics-enabled collections model 12 How machine learning can improve pricing performance

Obtaining fair compensation for complex payments products, such as corporate cards, merchant acquisition, and treasury management services, has long been a major challenge. This is primarily because these products tend to be complex, offered in myriad forms, and implemented across diverse markets. Treasury services, for instance, might have 1,000 or more different fees, and prices are often embedded in private contracts not shared within the organization. Throughout the payments industry, these problems are further complicated by ever-changing payments methods and platforms created by the rapid evolution of payments technologies. And now, expectations of rising interest rates are compounding the situation, increasing uncertainty in product pricing performance for both the short and long term.

Walter Rizzi However, there is also good news on the tech - Mining diverse data sets for deeper insight Z. Maria Wang nology front. Just as technological advances The complex nature of financial services Kuba Zielinski are reshaping the payments landscape, they are also delivering powerful new analytical presents substantial hurdles to those charged capabilities that have the potential to trans - with pricing strategically. Merchant acquir - form the way banks and other payments ing, corporate cards, and treasury services, providers price products and services. In fact, for example, often include hundreds of prod - early users are already reporting reduced vol - ucts, each with their own distinct fees. Serv - ume loss and customer attrition rates attrib - ice contracts also differ, and might begin and utable to their use of advanced analytics. end at different times. Moreover, prices tend

How effective is your current pricing strategy?

• When was the last time the company reviewed its current pricing strategy?

• Is the full market value of its products and services being captured?

• Does present pricing cover the marginal cost of each transaction?

• What internal and external data sources are being called upon to understand customer and prospect needs and behaviors?

• Does the company make full use of new technologies to mine deep customer and prospect insights from diverse data sources?

• Is the company applying appropriate disciplines and interactive tools to translate new - found insights into actionable strategies?

13 McKinsey on Payments August 2018 Exhibit 1 Treasury repricing programs are nearly as likely to be value destructive as value additive.

Change in overall client revenue from treasury repricing program

Negative client revenue impacts Positive client revenue impacts 43,591

37,355

29,170

23,829

16,626 10,800 13,415 2,758 7,882 8,307 4,301 3,694 8,178 4,612 2,035 2,258 5,751 3,122 2,036 3,603 1,489 2,354 2,445 2,510

) ) ) ) ) ) ) ) ) % % % % % % % % % % % % % % % % % % % % % % % % 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 1 2 7 2 5 5 4 3 8 6 9 0 0 ) 1 7 2 5 4 3 8 6 9 - 2 - - ( ------( 1 ( ( ( ( ( ( ( 1 1 2 0 - - - - 0 - - - - - < - - 0 - - 0 0 0 0 0 0 0 ) )

) ) ) ) ) ) ) 1 0 5 0 ( 1 6 0 7 2 4 3 8 5 0 0 0 0 0

0 0 0 0

2 0 9 5 2 5 7 3 8 4 6

9 0 1 ( 1 ( ( ( ( ( ( ( 1

(

Overall client treasury revenue change % Source: GCInsights; McKinsey Analytics to be set within the context of the respective New solutions are emerging to cope with client relationship and transparency within pricing complexity. Developments in com - the industry and individual institutions is fre - putational technology, data engineering, quently rare or nonexistent. In light of such and digitization of general processes can complexities, most payments providers strug - now transform how banks and other pay - gle to systematically devise fair and effective ments providers create and implement pric - price strategies. For instance, a recent McK - ing structures. Rapidly declining costs in insey study of treasury management services high-performance computing and data stor - in North America suggests that, over the long age, for example, are enabling them to use term, re-pricing services leads to value de - larger and more diverse data sets to build struction about as frequently as it does to more sophisticated analytical pricing mod - value creation (Exhibit 1). In the study, price els. Unsurprisingly, several industry leaders increases resulted in revenue declines a year are already capitalizing on the benefits of later at more than half of the subject institu - these developments . tions, suggesting the outcome of pricing ad - An especially useful new tool has been Spark - justments is highly unpredictable. Beyond. This application can automate fea -

How machine learning can improve pricing performance 14 ture engineering by creating a wide range of probabilities, price sensitivities, propensity variable transformations, and is highly effi - to churn, and life-time value. These new in - cient in identifying the most effective ma - sights allow management to identify micro - chine learning algorithms, such as random market segments, and thus target pricing forest or XGBoost. The application also en - more narrowly—down to the individual cus - ables users to export selected algorithms and tomer level when data permits. Closely attun - features for out-of-sample testing and other ing pricing to customer and prospect needs modeling needs in an external environment. maximizes price performance while minimiz - ing customer attrition and volume loss (Ex - Banks that adopted advanced analytics early hibit 2, page 16). on have been building massive data sets that integrate customer and prospect details Developing a holistic multi-step approach drawn from diverse internal and external data Although new tools and capabilities bring op - sources. The resulting content-rich data sets portunities for payments providers to signifi - are yielding deeper customer and market in - cantly enhance their pricing performance, sights that are unobtainable using traditional real success will only come through system - data. For instance, government-published atic and comprehensive execution. Achieving econometric data can yield economic wellbe - maximum effectiveness requires an enter - ing information and thereby better guide a prise-wide pricing transformation. One way bank’s budgeting process. And adding com - to initiate a pricing transformation is to de - mercial and benchmark data can help banks velop incremental price changes with se - more accurately determine their current busi - lected markets or segments using pilot ness share in large corporate relationships. To programs that can be quickly learned and it - obtain richer and more actionable insight at a erated before rolling out a broad pricing pro - granular level, some institutions are adopting gram. Once the proof-of-concept is a variety of advanced technological capabili - established, the full program can then be de - ties, including machine learning, deep learn - ployed through a three-step approach that ing, and more generally, artificial intelligence optionally can use early revenue gains to fund (AI). AI uses algorithms that range from unsu - subsequent steps. pervised (such as clustering and principal Step 1: Early transformation success tends to component analysis) to supervised (such as include, but not necessarily be limited to, the random forest and neural networks) to rein - following initiatives: forcement learning. • Using advanced analytics technologies, Some payments leaders are also venturing such as machine learning, to establish pric - into interactive digital pricing, either by sub - ing benchmarks at a granular level. For ex - scribing to third-party services or building ample, banks can drill down from their own digital pricing tools. Using new traditional segmentation levels (such as data sources, technologies, and modeling geography, industry, and deal size) to techniques, these early adopters are provid - postal code or business unit, and thus more ing front-end staff with in-depth views of cus - quickly identify fee leakage at the level of tomers and prospects, including such individual customer pricing. information as their product acceptance

15 McKinsey on Payments August 2018 Exhibit 2 An interactive tool can give relationship managers precise price recommendations, helping to minimize client attrition.

Cash vault coin order per roll Current GCI benchmark Target Volume price price price Current GCI Internal Target Potential price price benchmark price $0.13 $0.10 $0.13 $0.13 $0

Client priced at internal benchmark for this quartile

Transit check deposited Current GCI Internal Target Potential price price benchmark price $0.08 $0.11 $0.09 $0.09 $3

Client priced below both GCI and internal benchmarks, price increase up to internal benchmark

DDA deposit Current GCI Internal Target Potential price price benchmark price $0.59 $1.39 $1.30 $0.74 $739 Client priced below both GCI and internal benchmarks, price increase capped at 25% (still below internal benchmark)

Online information reporting previous day Current GCI Internal Target Potential price price benchmark price $0.04 $0.14 $0.05 $0.05 $352

Client priced below both GCI and internal benchmarks, price increase up to internal benchmark

Source: GCInsights; McKinsey Analytics

• In parallel with the above, developing in - ernance, and to identify and remedy flaws teractive tools that enable field represen - in current processes and policies. tatives to rapidly recognize pricing Initial programs incorporating these three el - opportunities in client portfolios, and to si - ements can yield revenue lifts of about 15 per - multaneously leverage other opportunities cent within six to nine months, yet incur only to expand share of business with the cus - minimal client and volume attrition rates. tomer. And those who implement rigorous service • Initiating price discussions throughout the re-pricing programs can apply early revenue organization and redesigning the pricing gains to funding the overall journey. process so it can be implemented in care - Step 2: Begin developing new organization- fully timed waves. A key component of this wide pricing skills and capabilities. While this is building a disciplined exception-man - often becomes a longer-term journey, it is agement process to strengthen pricing gov - one that needs to be initiated and proactively

How machine learning can improve pricing performance 16 Pursuing insights across the payments landscape

To better track the global payments industry, McKinsey frequently turns to GCInsights, its wholly owned research subsidiary. Since 2014, GCInsights has been acquiring and analyzing monthly or quarterly data from approximately 30 North American banks that provide commercial card and treasury management services. Its extensive data pool provides transaction-level details, such as price and volume information for approxi - mately 5 million pricing events across more than 250 service codes. These codes in - clude treasury management (such as DDA deposit) and commercial card (such as travel and entertainment purchases). Some interchange details, such as a merchant’s industry, region, and institutional size, are also included. Finalta, another McKinsey tool, provides global benchmarking services for the retail banking and insurance sectors. It covers ap - proximately 300 banks and insurers in more than 55 countries.

managed from an early stage. Step 2 com - address current and prospective client needs monly includes: in diverse markets and segments. Pricing tac - tics, for instance, can be finely tuned to re - • Improving and expanding skill sets flect evolving customer and prospect needs throughout the pricing organization by drawing on a variety of pricing approaches, • Significantly enhancing current pricing including bundled pricing, subscription pric - data sets ing for small businesses, and unbundled gran - • Building strong pricing analytics capabili - ular pricing for corporate clients. Conse - ties quently, deeper understanding of ever-changing marketplace needs can provide • Developing enterprise-grade tools to assist a clear competitive advantage. in such key areas as new-deal pricing, con - tract renewal pricing, and ongoing revenue For example, when helping a global pay - portfolio management ments-network provider to develop a new pricing strategy, McKinsey heavily adopted Common related investments include acquir - machine learning while applying this three- ing new technology capabilities, such as voice step approach. Drawing on the large amount recognition and automation, to reduce man - of transactional data from the last few years, ual processing, human error rates, and tech - McKinsey designed a new pricing construct, nical leakage. and subsequently simulated its implementa - Step 3: Lastly, equipped with powerful ana - tion to determine the probable impact on lytical tools, immense data sets, and new - both revenue and attrition. Simulation played found skills, banks can continually enhance a central role in forming the new pricing their pricing strategies through ongoing strategy. monitoring and scaling of new pricing con - In another case, a global merchant services structs. Together, these actions are helping company needed to more closely match its es - many institutions to improve the ways they tablished pricing with the current value of its

17 McKinsey on Payments August 2018 product and service offerings. Using gradient ance, by contrast, also requires the applica - boosting machine (a machine-learning tech - tion of sound business principles and disci - nique) proved to be highly effective in identi - plined practices. fying and realigning numerous mismatched Aside from aspects of data and analytics mod - price-value occurrences while accounting for eling, another common obstacle to achieving the current competitive environment. full effectiveness in the use of advanced ana - Notably, this framework is expandable to fit a lytics is a siloed organizational structure. Or - wide range of pricing scenarios. In small-to- ganizational silos often lead to departmental medium enterprise lending, for instance, misalignments—say between finance, mar - McKinsey worked with UniCredit to devise a keting, and sales—when making strategic value-based account management strategy; a pricing decisions. In these situations, the best core component of the effort was developing practice is usually to ensure from the outset machine-learning models and a custom user that all stakeholders have integral roles in interface that generates client benchmark planning and implementing the pricing trans - profits. The customized solution enables re - formation, and participate regularly in trans - lationship managers to simulate various deal formation planning and progress review scenarios at the full client relationship level meetings. in a test-and-learn environment to ensure To generate positive results even the best of continuously monitored and improved re - strategies require seamless execution. Real or sults. These changes resulted in value in - perceived flaws during the roll-out of a new crease of up to 15 percent. pricing can quickly incite rejection among re - Leaping the hurdles of price lationship mangers—a problem that success - transformation ful institutions are overcoming by Adopting machine learning and advanced an - showcasing success stories and prominently alytics generally gives payments providers recognizing champions of change within their significant power to reshape their longstand - organizations. Of course, it is also essential to ing pricing strategies, but transformation can promptly realign performance incentives also present unique challenges. with new pricing approaches and goals. En - Advanced analytics presents a variety of so - gaging relationship managers in codeveloping phisticated tools, but their effectiveness de - pricing strategies is a highly effective way to pends largely on how the insights are actually generate positive change attitudes. To instill derived and subsequently used. For example, manager confidence in a new pricing ap - traditional approaches to setting pricing tar - proach, one European bank devised algo - gets, such as scoring or ranking customers on rithms that can instantly show managers the price sensitivity, are less actionable than em - bank’s pricing structure on comparable deals. ploying a mathematical model that links offer Advanced analytics technologies are begin - acceptance probability to historically ac - ning to rapidly alter how businesses operate cepted offer rates. Pricing models based around the globe. Given the central role of solely on statistical performance can deliver banks and other payments industry partici - suboptimal guidance: maximum perform - pants, they are fast becoming subject to those

How machine learning can improve pricing performance 18 same forces, and to remain competitive will proaches to pricing, but those willing to adopt therefore need to embrace them on a timely a comprehensive pricing transformation built basis. Many in the payments industry might on deep market insights will clearly be among be hesitant to change their longstanding ap - tomorrow’s industry leaders.

Walter Rizzi is a partner in McKinsey’s Milan office. Z. Maria Wang is an analytics expert and Kuba Zielinski is a partner, both in the Boston office.

Robust analytical tools are available to help banks increase revenue at each phase of the customer life cycle. The next issue of McKinsey on Payments will include an article examining how treasury management service providers are using analytics to optimize portfolio value, with special attention to product structure and pricing, cross-selling, and managing attrition.

19 McKinsey on Payments August 2018 Combating payments fraud and enhancing customer experience

The fraud threat facing banks and payments firms has grown dramatically in recent years (Exhibit 1). Estimates of fraud’s impact on consumers and financial institutions vary significantly but losses to banks alone are conservatively estimated to exceed $31 billion globally by 2018. Several converging trends have propelled the increasing scale, diversity, and complexity of fraud. Vulnerabilities in payments services have increased as the shift to digital and mobile customer platforms accelerates. New solutions have also led to payments transactions being executed more quickly, leaving banks and processors with less time to identify, counteract, and recover the underlying funds when necessary. Finally, the sophistication of fraud has increased, in part through greater collaboration among bad actors, including the exchange of stolen data, new techniques, and expertise on the dark web.

Salim Hasham Increasingly agile fraud perpetrators have dustry verticals in cyber and data analytics Rob Hayden benefited from banks’ and payments firms’ has created a ready supply of talented, cross- Rob Wavra limited ability to adapt. While most institu - disciplinary resources unencumbered by tions have well-funded anti-fraud groups, key legacy organizational structures. Today’s resources are often fragmented across the or - challenge is harnessing these components to ganization. Essential data, investigative and reduce current losses, detect and prevent forensics expertise, and analytics talent are emerging fraud, and enhance customer ex - typically distributed across cyber, compli - perience . ance, legal, IT, and fraud teams, with little to The shifting fraud landscape no coordination or data sharing. Fraud is not only growing but evolving (Ex - Effectively combating fraud through analyt - hibit 2, page 22), forcing countermeasures to ics requires a mindset shift from a narrow shift from the transaction-centric assessment focus on false positives and loss prevention of fraudulent charges on a card or doctored to an appreciation that the same technologi - checks deposited at an ATM, to preventing, cal advancements making fraud more perva - detecting, and remediating increasingly so - sive also enable the tools and environment phisticated, long-term sleeper frauds and ex - to address it. With their shift to digital serv - otic concerns like manipulated synthetic ices, banks have access to exponentially identities. Some tactics have worked, with more customer and transaction data than in Visa estimating that chip technology reduced the past. New technologies create the means counterfeit card fraud in the US by 66 percent to more accurately segment customers by for EMV-enabled merchants in June 2017 risk, enabling lower-friction digital experi - compared to June 2015. Other typologies ences—and higher satisfaction levels—for (“abuse cases”) of fraud remain without ef - low-risk customers. And the explosion of in - fective countermeasures, straining tradi -

Combating payments fraud and enhancing customer experience 20 tional anti-fraud efforts, generating increas - sources that expand customer identifica - ing losses, false positives, and negative cus - tion beyond knowledge-based authentica - tomer experiences: tion (KBA), analytics to detect emerging trends and high-risk access events, and • Account takeover (ATO) is the theft or customer experience-sensitive authentica - misuse of credentials to fraudulently gain tion journeys, limiting customer chal - access to an existing customer account. lenges based on risk segmentation and This can be a one-time funds transfer other triggers. event or an ongoing access exploitation (e.g., adding a registered user, changing the • Synthetic identity, a scenario in which contact email or mailing address) for crim - fraud perpetrators combine fragments of inal purposes. Successfully combatting stolen or fake information to create a new ATO requires a mix of nontraditional data identity and apply for financial products, is

Exhibit 1 Fraud is on the increase in the US.

Average number of fraudulent transactions attempted per merchant per month1

442 Average successful 34% p.a. attempts

333

298

185

206 156 133 91

2013 2014 2015 2016

1 Weighted merchant responses to LexisNexis survey question: In a typical month, approximately how many fraudulent transactions are prevented by your company / successfully completed by fraudsters? What is the average value of successful fraud transactions? Source: US Department of Commerce; LexisNexis The True Cost of Fraud study, 2016

21 McKinsey on Payments August 2018 Exhibit 2 Payments transaction fraud takes many forms.

Fraud types/threats Key examples Trends impacting this fraud type

Account fraud Account creation using false or stolen Increasing sophistication of tools (new or takeover) identity used to establish identity Fraudulent access to an existing (e.g., IP/address geolocation, record account (e.g., adding a registered user, matching algorithms) changing email or mailing address)

Transaction/ Transactions using stolen Increases in card-not-present payments cards/accounts fraud as EMV is rolled out Identity fraud Suspicious transactions authentication measures theft (e.g., New geography, counterparties) (e.g., biometrics) and algorithms being rolled out Existing customer fraudulently Improved tracking methods for disputes a charge (e.g., denies delivery Chargeback delivery while retaining the goods) fraud

Unintentional chargeback fraud More automation, less manual (e.g., forgetfulness, accidental order, customer service Friendly not recognizing merchant name, Increased ‘on-demand’ fraud misunderstanding return policy, family ecommerce members ordering)

Increasing threats

Source: McKinsey analysis

of growing concern in light of data exposed • Business email compromise invokes so - through the 2017 Equifax breach. All of cial engineering to lure an empowered em - synthetic identity fraud’s forms—tradi - ployee to initiate a transfer to the tional (a fusion of valid information from fraudster’s account, usually at the apparent multiple real people), manipulated (all real request of an executive. A similar phenom - information about a single person with a enon, invoice redirection , leverages social fake national ID/SSN), and manufactured engineering to alter payment information (wholly fake information, including na - for legitimate payables accounts (often by tional ID/SSN)—can exist only because of claiming a new bank account has been inadequate onboarding and customer due opened), redirecting payment to a fraud - diligence. Filling these gaps will require ster’s account. These are growing fraud cross-functional collaboration across lines categories—business email compromise of business and functional silos, expanded alone causes nearly $1.5 billion per year in external data for validating multiple ele - losses according to the FBI—demanding ments of customer applications and scor - institutions respond with tailored front- ing their likelihood of authenticity, and the line training, re-architecture of existing fusion of these external sources and exist - controls (e.g., who can change payment in - ing internal customer data. formation and based on what information,

Combating payments fraud and enhancing customer experience 22 verification of new information with a often hurt customer experience more than known contact), sophisticated analytics to they mitigate fraud. flag risky changes before payments are Fraud interventions driven by advanced ana - made, and new data and technologies like lytics tend to follow a few archetypes: voice analytics. • Predictive detection, encompassing user au - In “Fraud management: Recovering value thentication (e.g., determining whether the through next-generation solutions” ( McKin - transacting party is in fact a customer), sey on Payments, June 2018), our colleagues customer due diligence (e.g., low/high-risk identified three concrete steps to effectively fraud profiling as a factor in exception de - redefine fraud operating models to fight cisioning), and transaction risk (e.g., these emerging threats: re-engineering fraud whether hallmarks of fraud are present in case management; redesigning journeys to the context of other transactions for the improve the customer experience; and em - account, customer, and household). This ploying advanced analytics. Given the vast can come in the form of in-house custom potential of advanced analytics in the fraud analytics models, commercial off-the-shelf arena and the significant barriers to its effec - software-enabled detection, or public part - tive use, we will focus on this critical dimen - nerships with emerging technology compa - sion. When done well, analytics can nies, like HSBC’s relationship with Ayasdi. consistently reduce fraud losses by 3 to 5 per - cent in mature environments and by over 30 • Enhanced internal process efficiency , such percent in evolving contexts. And yet we have as capacity forecasting and providing ana - seen even the most advanced firms struggle lysts with context detailing the reasons a to attract and maintain analytics talent, tran - transaction failed an initial screen. scend organizational and disciplinary bound - • Automated fraud triage and other robotic aries to deploy the best solutions, and process automation (RPA). The London transition from analytics test cases to pro - School of Economics examined 16 case duction capabilities. studies of RPA, finding first-year returns False starts on investment of 30 to 200 percent. The In the face of the continuous evolution and longer-term value—including enhanced increasing pace and volume of fraud threats, compliance and the reallocation of em - fraud teams find themselves hamstrung by ployees to higher-value tasks—is likely ineffective triage of alerts, poor data quality, even greater. and non-existent or outdated intelligence. Many banks, however, have faced serious Compounding this fragmentation, many in - challenges when attempting to effectively in - vestments in fraud-related artificial intelli - tegrate advanced analytics into their fraud gence can be characterized as “science defense. Common pitfalls include: projects,” lacking the scale to deliver enter - • Building models that do not take advantage prise impact. In the meantime, institutions of all available data, overlooking siloed risk are dedicating additional resources to manu - scoring inputs residing in cyber, customer ally wade through low-value alerts or building relationship and product sales groups. increasingly aggressive rules and models that

23 McKinsey on Payments August 2018 Such inputs can be as simple as determin - which are difficult, if not impossible, to in - ing whether cross-ownership of mortgage terpret. Techniques like Locally Inter - or card products correlates to lower fraud pretable Model-Agnostic Explanations risk or exploiting device geolocation data (LIME) provide some insights into sophis - to inform mobile deposit fraud screening— ticated models, but do not mitigate the in - which enabled a US bank to identify de - creased model risk that the push for posits typologies with higher fraud performance and innovation has created. incidence of 25 to 1,000 times. More ambi - • Not accounting for the increasing interest tious enhancements include holistic re - of regulators in fraud models. This scrutiny alignment of a bank’s financial crime is likely to accelerate, given the opaque na - structures, people, and technology, as un - ture of fraud rules and concern over dertaken by HSBC in 2015 with its creation whether they impose disparate impact on of a unified Financial Crime Threat Miti - members of a protected class. Loss ratios gation organization. and raw statistical performance cannot be • Deploying “crime- and institution-igno - the only metrics by which modern fraud rant” models, which are statistically com - models are measured. pelling but hobbled by a lack of • Grafting advanced analytics tools onto ex - understanding of underlying fraud mecha - isting processes and policy frameworks nisms, institutional controls, and interven - rather than leveraging analytics to trans - tion options. While staffing fraud analytics form the business. Analytics should not be efforts with cross-disciplinary teams of deployed merely to dig out of a false posi - data scientists, data engineers, translators, tive hole created by bad policies and ineffi - and financial crime and fraud subject mat - cient processes. While many frauds are ter experts is a powerful solution, Citi - driven by control weaknesses, fast-growing group went one step further, empowering a threats like synthetic identity fraud exist permanent Global Investigations Unit to only because of insufficient onboarding proactively analyze and combat emerging processes and customer due diligence at financial crimes with a full range of experts the application stage. Using advanced ana - and technical staff. lytics to detect these frauds or reduce false • Not addressing the growing model risk positives being generated misses the real management (MRM) demands in fraud opportunity to fix outmoded policies and mitigation. The increasingly opaque and underperforming processes. sophisticated models used to detect fraud The best analytics interventions leverage and the rapid pace at which fraud is evolv - cross-disciplinary expertise, fusing analytics ing combine to create model risk. Some with deep industry and client organization causes are easily addressable—assump - context. At a regional bank in the United tions about the markers of fraud and the States, the breakthrough came from shifting its scale of potential losses can become focus from identifying fraudulent transactions strained—but others stem from well-mean - to minimizing dollar losses from a specific ing attempts to use cutting-edge deep fraud typology. Pairing this approach with risk- learning and neural network algorithms

Combating payments fraud and enhancing customer experience 24 Accelerating analytics-driven fraud defenses

“Money mule” accounts are often recruited via unwitting accomplices (e.g., through work-from-home schemes) and exploited to launder illicit funds, rapidly moving sums through multiple accounts to obfuscate sources and frustrate identification and repatriation efforts. Advanced network analytics and machine-learning techniques can discern pat - terns in the noise, exposing suspicious accounts with impressive efficacy. For instance, QuantumBlack, a McKinsey company focused on advanced analytics, analyzed over 18 billion transactions across multiple banks, creating a “mule-inesque” score integrating indicators of mule activity (e.g., account age, economic relationships, direct debit frequency). QuantumBlack analyzed over 10,000 suspected criminal account networks through an investigator ana - lytics support tool, visually tracing dispersion networks to allow for real-time detection and timely repatriation. The ex - ercise ultimately identified 15,000 mule accounts across multiple banks. Although signature fraud has been a common tactic for generations, it has taken on new dimensions in certain mar - kets. A bank in Latin America was overwhelmed by both traditional loan application fraud (e.g., for recently deceased relatives) and “auto-fraud,” where an applicant intentionally modifies their own signature with the intention of later claiming not to have initiated the loan. Using deep learning-based image analytics techniques, McKinsey identified the subtle indicators of both types of fraudulent signatures. The new model improved fraud detection by over 31 per - cent when compared to the bank’s existing model.

driven policy changes and data science-driven crease the speed and efficiency of anti-fraud enhancements to tune their detection model, processes. And the explosion of the cyber and the bank was able to create a combination of data analytics verticals has created a ready model enhancements and policy change efforts supply of talented, cross-disciplinary re - projected to reduce annual losses in the target sources unencumbered by legacy organiza - category by over 32 percent. tional structures.

Succeeding in fraud analytics Analytics provide a unique and powerful Effectively deploying analytics to combat means to transform fraud operations. The fraud requires a shift in thinking from a nar - most successful fraud analytics programs are row focus on false positives and losses to an designed to be: appreciation that the same trends making • Business-back: Anti-fraud analytics ef - fraud more pervasive also enable the tools forts must be built on a unified, cross en - and environment necessary to combat it. terprise foundation, breaking down silos With their shift to digital services, banks have between channels, products, and fraud exponentially more customer and transaction types. This is usually best accomplished data than in the past. New technologies also with an overarching fraud operations create the means to more accurately segment transformation mandate from senior man - customers by risk, enabling lower-friction agement, transcending analytics. Given the digital experiences—and higher satisfaction increasing impact of fraud on bottom lines levels—for low-risk customers. Many of the and reputations, the business case to se - technological advances that have sped the cure such a broad mandate should be fairly pace of payments can also be leveraged to in - straightforward. The goal should be a

25 McKinsey on Payments August 2018 process seamlessly integrated across the analytics built on well-founded indicators fraud lifecycle, incorporating data span - of crime. This creates the means to spot ning business units and functional silos to broader patterns of suspicious behavior— create a holistic view. such as campaigns by criminal networks as opposed to lone fraudsters—and to look for • Criminal-forward: Applying a criminal emerging fraud typologies before signifi - mindset to fraud analytics—a common tac - cant losses result. tic used by law enforcement agencies—can provide inputs to better understand the mo - • Customer-focused: While constantly tivations and methods of perpetrators of evolving to counter the fraud threat, coun - fraud. From this starting point, models can termeasures should be designed in ways be designed to predict, prevent, and detect that create a distinctive customer experi - crime based on powerful data-driven in - ence balancing trust and convenience to ac - sights and expert-created indicators created celerate insight into fraud. Analytics should from more nuanced and comprehensive un - play as critical a role in facilitating low-risk derstanding of the criminal. By mapping ty - customers and transactions as they do in pologies to indicators of fraud, analytics can thwarting potential fraud, enabling institu - be better targeted and prioritized. Such a tions to create customized, analytics-in - focus requires more than just fraud experts formed journeys balancing security and and data scientists; it demands a rigorous, convenience. Models must be built on the evidence-based method to testing expert proper foundation, integrating customer hypotheses with large data sets on past behaviors across accounts and transactions fraud and a culture that embraces the power into a single view that enhances the power of such a hybrid approach . of prediction and detection.

• Intelligence-driven: Rather than building Cutting-edge efforts integrate these themes, models that chase historical fraud threats pairing a mandate to improve customer expe - after the fact, banks must continuously rience with improvements in fraud identifica - evolve their analytics-centered defenses tion. One global bank undertook such a based on detailed up-to-the-minute under - hybrid effort, redesigning customer authenti - standing of the criminal environment. Such cation journeys in its digital channel to si - knowledge is best developed through intel - multaneously improve its confidence in ligence operations and sharing, including customer identification while dramatically monitoring of the dark web. Rather than in - improving experience. Beyond achieving its terrogating fraud incidents in isolation, in - security-related goals, this effort reduced stitutions must take a broader look at the costs related to customer lock-out by $5 mil - patterns of crime. Industry-wide objectives lion and improved Net Promoter Scores in such as FS-ISAC in the United States pro - the online channel by 29 points. vide a more robust data set from which to Getting started identify such patterns. The goal should be To get the most from advanced analytics, or - to shift risk identification from regulatory ganizations should begin by clearly articulat - rules-based detection and predictive mod - ing their operational objectives. This critical els built on past frauds to forward-looking

Combating payments fraud and enhancing customer experience 26 foundation provides the proper screens Individual use cases, pilots, and other tradi - against which to evaluate analytics efforts and tional means of intervening through analytics investments. It also aligns analytics interven - should be used to enhance these base capabil - tions with business unit goals, identifying the ities and push the institution’s capacity, core decisions requiring analytics support, rather than simply as a means to deliver point prioritizing those decisions best informed by solutions. In the medium to long term, organ - advanced analytics, mapping data to inform izations must build organic capabilities to those decisions, designing models leveraging constantly reassess evolving fraud threats, re - that data, and establishing the metrics against visit and improve the operating model, and which to evaluate analytics success. design fit-for-purpose advanced analytics and fused data sets. Building from this base, firms should ap - proach advanced analytics as a transforma - * * * tion rather than a one-off event. In the near The perpetrators of fraud are highly adept at term, this involves a focus on: exploiting advances in technology, collabora - • Identifying the universe of possible inter - tion, and specialization. Legacy approaches ventions, connecting the business with ana - to fraud prevention have not kept pace, with lytics and compliance to prioritize based on financial institutions stubbornly dependent potential impact and technical feasibility. on siloed data and manual processes. Banks • Articulating clear operational goals, under - and payments firms looking to establish a standing where internal analytics capabili - competitive edge—and avoid increasing loss ties stand today, where they should be, the exposure and mitigation expense—must har - investments required, and developing plan ness these same trends. Advanced analytics to transition from outside support to a re - provide a tangible reason to integrate data liance on internal resources. across siloes, a means to automate and en - hance expert knowledge, and the right tools • Cataloguing current capabilities and en - to prevent, predict, detect, and remediate suring they are being leveraged to their fraud. Analytics is not an overnight fix, but it maximum potential. Banks often have can pay immediate benefits while creating many of the tools required for an effective the foundation for anti-fraud operating mod - initial defense but have not yet aligned els of the future. them properly.

Salim Hasham is a partner in McKinsey’s New York office, and Rob Wavra is an expert associate partner with McKinsey’s QuantumBlack in Boston. Rob Hayden was a senior expert in McKinsey’s Cleveland office. Rob passed away suddenly and unexpectedly earlier this year, and is deeply missed by all whose lives he touched. Please see McKinsey on Payments, Issue 27 for a remembrance of Rob.

27 McKinsey on Payments August 2018 Using data to unlock the potential of an SME and mid-corporate franchise

Banks have long pondered the untapped value of the commercial segment but often lack the means to identify the precise needs of individual companies in this large and diverse population. This is changing, however. By mining huge reserves of customer data, banking analytics leaders are meeting the needs of hundreds of thousands of commercial customers—from small businesses to medium-size corporations—with new levels of convenience and cost efficiency. Several banks have achieved a ten-fold increase in the success rate of product recommendations, thus delivering highly relevant offers with clear economic benefit. This article highlights recent examples of how “next-product-to-buy” (NPtB) recommendation engines are identifying time-critical needs for their small- and medium-size enterprise (SME) and mid-corporate clients.

Ignacio Crespo The problem: Most businesses are Bank in the UK; ING, Banco Santander, and Carlos Fernandez “invisible” BBVA in Europe are just some examples of Huw Kwon Many banks currently use rules-based models banks improving their commercial perform - to generate recommendations for SMEs and ance by leveraging machine learning. These mid-corporate companies (with annual sales advanced techniques have proven effective in up to $100 million), but with limited success. diverse customer segments, from self-em - Relationship managers often view these rec - ployed individuals to large corporate cus - ommendations with skepticism, as conver - tomers. SMEs and mid-corps are the sion rates typically range between three and sweet-spot for NPtB, as they generate mas - five percent. They resort to general proposi - sive amounts of data, which are typically un - tions designed for the consumer segment and derused. With the help of advanced analytics devote most of their energy to those clients decisioning engines, banks have demon - whose businesses they already know well and strated that it is now practical to mine vast whose needs they can anticipate reliably. The (and often messy) amounts of data, separat - result is that 25 percent of a bank’s commer - ing signal from noise, to arrive at precise rec - cial customers usually account for 85 percent ommendations for a client’s next action. In of the revenues, and the remaining 75 percent addition, by broadening the types of data col - represents the “long tail” of untapped poten - lected for the commercial segment, banks are tial. These companies are effectively invisible also analyzing customer behaviors, transac - to the bank’s sales force (Exhibit 1). tions, and customer preferences across more The solution: Anticipate the client’s extensive databases. next step Successful implementation of NPtB engines Banks are investing in building up predictive has boosted new sales upwards of 30 percent models globally: US Bank and TD Bank in and increased commercial segment revenues North America; Itau and Banco do Brasil in by between two to three percent. The impact Latin America; Barclays Bank and Lloyds on sales efficiency has been radical in some

Using data to unlock the potential of an SME and mid-corporate franchise 28 cases, with an increase of more than 50 percent (SVD) to classify customers according to pat - in the number of leads offered per client and as terns in their purchase histories, each pattern many as six out of ten customers purchasing a culminating in a target output, that is, the new product in response to a sales call . “next product to buy.” The number of inputs and the complexity of the algorithms used to Leveraging data for NPTB recommendations analyze these inputs have been increasing in More than a decade ago, Amazon and Netflix recent years, achieving outputs that have began leveraging data and analytics to im - much greater precision than was possible prove their cross-selling efforts. They started with next-best-action (NBA) models. (See with simple analytics, dividing huge customer sidebar on page 33 for a summary of the evo - populations into several dozens of microseg - lution of NPtB from NBA.) ments according to key behaviors (inputs). In The NBA engines employed by Amazon (with order to achieve this new level of precision, more than 300 million customers reported in they used singular value decomposition 2016) and Netflix (125 million subscribers re -

Exhibit 1 Cross-selling efforts often focus on the companies that relationship managers know well.

Client Strong Basic Limited relationships categories relationships relationships Limited client knowledge RMs have deep Average client Fully reactive approach client knowledge knowledge (“I just call for renewals”) Proactive offerings Opportunistic, almost “campaign-like” approach Revenue per 25% 25% 50% client of total clients of total clients of total clients

0355 104515 20 2530 40 50658555 60 7095751008090

Products per client ~14 ~7 ~4

Percentage of revenues 85% 10% 5%

The “long tail”: smaller/low share of wallet Source: McKinsey analysis

29 McKinsey on Payments August 2018 ported in the first quarter of 2018) are not en - history. For example, one bank in Europe tirely suited to banks serving SMEs and large started by consolidating the information it corporations with products/services that ad - had for 1.3 million SME customers, ranging dress a relatively narrow range of business from beauty salons, doctor’s offices, and fam - activity—domestic and cross-border pay - ily-owned stores to small manufacturing ments, financing, documentary credit, invest - companies and technology start-ups. This ments, and insurance. By building NBA data set yielded 1,200 variables for analysis. recommendation engines designed specifi - Continuing the focus on internal data, the sec - cally for transaction banking, banks have in - ond wave introduces algorithms capable of di - creased service levels and profitability, gesting unstructured data (e.g., call records, improving their responsiveness to SMEs and email communication), as well as a broader helping large corporate clients cut through range of structured data from CRM systems complex banking relationships and account (e.g., share of wallet, historical risk scoring, structures to optimize liquidity. maturities, customer relationship lifecycle, To maximize the impact of each recommen - company value chain, and suppliers). Fast- dation, decision engines should identify both evolving algorithms augment the value of data customer needs and the preferred channel(s) already at hand by learning to recognize unan - for delivering the proposal and related com - ticipated clusters and associations in increas - munication. In some markets, companies ingly complex data sets. The algorithms tend to rely more heavily on direct communi - generate actionable insights into a company’s cation with relationship managers, who play a current needs, from payables management to key role in following up on recommendations. financing for new equipment, based on infor - In other markets, such as the Nordics and the mation coming from transactions and pay - UK, the digital channel is the primary means ments along the customer value chain. both for alerting a customer to a recom - The third wave analyzes a broad range of data mended action and for delivering more de - from point-of-sale transactions to industry tailed information about the opportunity. news and comments on social media to gen - Consolidate data for analysis in three erate ever more precise recommendations. As waves machine learning algorithms become more The data reserves required to power an NPtB sophisticated, it is possible to produce recom - engine are consolidated in three waves. As the mendations from increasingly diverse types volume and complexity of data increase of unstructured data (including, voice, image, across the three waves, analytical algorithms and video files) extracted from industry and become progressively more sophisticated and company web sites, as well as news and social accurate in predicting precise, time-critical media (Exhibit 2). needs of individual customers. How to build an NPtB engine: Design, The first wave starts with the aggregation and develop, deploy analysis of internal structured data of various The preparation of an NPtB model moves formats, including customer demographics, through three phases: design, development, product usage, profitability, and transaction and deployment. Clear milestones mark the

Using data to unlock the potential of an SME and mid-corporate franchise 30 advance from analytics to proof-of-concept to (that is, data consistency, format valida - implementation in the front line. tion) to identify points for improvement and to restructure data ingestion (for ex - Design the NPtB engine ample, defining treatment of unavailable This first phase is the preparation and design values and primary keys, synchronizing of the NPtB engine—adjusting the scope, time periods). mapping pitfalls, and elaborating the busi - ness case to convince stakeholders through - 2. Ensure the IT infrastructure meets the out the organization of the value of the effort. processing requirements to run the This phase has five key activities: model. It is important to design the rec - ommendation engine so that it runs effi - 1. Prepare the data. Looking across the ciently on the current IT infrastructure. three waves of data consolidation, the first Most banks have adequate processing and step is to identify the types and sources of storage capacity to run basic NBA models data and map the variables that can be ana - that will generate actionable recommenda - lyzed. The goal is to locate data that can be tions. For example, a CPU system with ex - combined with unique customer identi - panded memory capacity is adequate to fiers and provide sufficient record history run induction algorithms based on deci - (at least two years of data). It is also neces - sion trees. However, deep learning algo - sary during this phase to check data quality

Exhibit 2 Data from diverse sources are consolidated in successive waves of increasing complexity.

“Small data” “Big data”

Explore external (non-) traditional data Supplement through Partner data external traditional data Enrich with internal Industry data Company web pages non-traditional data Leverage internal Share of wallet Government data Social media traditional data Customer Historical risk scoring Partner data etc. demographics Product usage Call records Credit bureaus and Þnancial databases Customer proÞtability Email records 1st wave of advanced Transactional data Customer life-cycle stage analytics use cases 2nd wave etc. 3rd wave

Source: McKinsey analysis

31 McKinsey on Payments August 2018 rithms require a GPU system to support NPtB model. The goal is to identify the prod - complex artificial neural networks. ucts a customer is likely to buy, prioritize rec - ommendations, and determine the most 3. Identify business sponsor and form effective channels for delivering an offer. The multidisciplinary team. The sponsor team works toward these goals by building al - should be a business-line owner author - gorithms to answer three main questions: ized to make binding decisions. The team should include data scientists, data engi - 1. Which products does a particular com - neers, business translators, UX designers, pany need or is willing to acquire? The and data architects. The team identifies NPtB analytical engine identifies opportu - the variables to be analyzed and builds the nities for cross-selling. For each specific analytical model based on precise under - company doing business with the bank, the standing of business goals, including famil - engine ranks commercial leads for each iarity with the market segment to be product according to two criteria: proba - addressed and the products to be targeted. bility (which leads are most likely to result in a transaction?) and value (which will be 4. Scope the effort and build the business most profitable?). case. The executive team must reach a shared understanding of the problem to be 2. Which companies need a particular solved and agree on a “back of the envelope” product? Next, the engine also prioritizes business case for the NPtB effort. It should clients according to their potential also identify the main elements for evalua - value/business priorities, propensity to tion (model performance, adoption rate, buy, and more. (This step is particularly conversion rate improvement, etc.). helpful for relationship managers, who must decide how to prioritize follow-up 5. Identify potential roadblocks. It is cru - calls and visits.) cial to follow proper legal and compliance procedures to ensure that the bank has the 3. Which channels should be used to opti - necessary consent/permission to use and mize the success rate of the commer - merge the targeted data. In our experience, cial opportunities? Leads are distributed most of the data available for the corporate to digital and traditional channels based on segment can be leveraged for NPtB analy - company behavior and preferences, con - sis; however, it is important to identify tact policies, and relationship managers’ early in the design phase any business limi - commercial activities. tations that may delay implementation. To answer these three questions precisely, Such limitations may include, for example, banks can analyze customer data, such as business involvement, change management payments transactions and digital interac - challenges, and workers’ council policies. tions. Machine learning algorithms can iden - Develop the analytical solution tify patterns in past customer behavior to In the development phase, data scientists, predict future customer purchases. Data sci - data engineers, and business translators col - entists build NPtB engines leveraging model - laborate to build the analytical solution of the ing environments such as R, Python, or Spark.

Using data to unlock the potential of an SME and mid-corporate franchise 32 From NBA to NPtB

Next-product-to- buy (NPtB) models represent a significant advance over those using first- and second-generation next-best-action (NBA) methodologies. In the past decade, product recommendations for corporate transaction banking were generated through statistical analysis of historically observed behaviors across diverse sub-segments (usually between 10 and 20 in number). Each recommendation called attention to a particular area (e.g., disburse - ment services, accounts receivable and working capital management, investments, trade finance, foreign exchange) and targeted broadly companies sharing general characteristics, as defined by a limited range of profile data, for ex - ample, company size, geography, industry, supply chain position, financial behaviors. With the proliferation of data points, second-generation NBA recommendation engines required more intensive work to describe the data, that is, to teach machines the data features to look for when analyzing a data set. This enabled banks to identify specific products (e.g., receivables financing, FX hedging, corporate procurement cards) that a cus - tomer would likely be considering. However, the analysis behind these recommendations focused on behaviors within specific cash-management functions, reinforcing the narrow scope of product “siloes,” with little opportunity to opti - mize financial performance across the full value chain. The goal of feature engineering, therefore, is to find the best combination of variables to enable a learning algorithm to recognize meaningful patterns in a particular data set. It is a key process in developing a conventional NBA model and until recently was a time-consuming manual process drawing on deep knowledge of business practices. Drawing upon a much broader set of structured and unstructured data and taking advantage of recent gains in pro - cessing capacity, today’s NPtB engines generate recommendations that are much more granular and precise than is possible with NBA models. The improvement in analytical sophistication and processing power comes thanks to deep learning, which can develop highly accurate predictive algorithms. Neural networks make it possible to auto - mate the discovery of data features and the identification of the best combination of features to produce the targeted prediction. These calculations produce remarkably precise predictions and deliver actionable results.

While deep-learning algorithms generate the Deploy and pilot NPtB engine most accurate predictions if data sources are The last phase is to embed recommendations complex and unstructured, gradient boosting in digital channels and relationship man - machines, random forest algorithms, and even agers’ interactions with clients. Banks test logistic regressions provide valuable commer - and refine the NPtB pilot in the field before cial opportunities for the NPtB engine. In addi - rolling it out to the full market. tion, gradient boosting machines have the A European bank recently tested an NPtB en - advantage of generating reliable recommenda - gine in five branches to evaluate the precision tions with smaller data sets or data sets where of sales leads. A team of relationship man - there are gaps. These models are trained with agers, product specialists, and branch man - past data and statistically back-tested on an agers participated in the pilot, which out-of-sample and out-of-time customer base included training on how to follow up on rec - to quantify model performance. ommendations and testing the effectiveness

33 McKinsey on Payments August 2018 of leads with clients. Over three months, the evaluating the sales process, such as number of team tested the leads with companies and visits, percent of leads used by relationship provided feedback. A sceptical relationship managers, conversion rates, and the level of manager selected an offer for a letter of guar - satisfaction among relationship managers par - antee recommended for a particular client, ticipating in the pilot (versus control group). In and commented, “I don’t believe the cus - addition, the pilot phase is the time to begin tomer will buy this, I know the company.” testing long-term performance metrics (in When the relationship manager asked the order to ensure sustainability in the front line), company owner if he needed a letter of guar - for example, hit rates for branch staff and rela - antee this month, he answered, “How did you tionship managers, customer profitability, and know? I am currently negotiating this prod - customer satisfaction. uct with another bank.” In the transition from pilot to full roll-out, it In the course of a similar pilot with another is crucial to ensure that the organization is financial institution, the model predicted aligned around the NPtB use case and that a that 4,500 companies, for which there was no support team is assigned for the deployment. indication in the data of previous interna - Adapting an NPtB engine to serve large tional trade activity, would purchase interna - corporate clients tional trade products in the coming month. Banks have also been able to improve the rel - As it happened one in five of these companies evance and timeliness of their recommenda - purchased an international trade product for tions to large corporate clients. At many the first time within 30 days. Based on the institutions, relationship managers are thor - performance of the pilot, the analytics team oughly familiar with the general needs of updated the model before implementing it their corporate customers, but sometimes across the entire organization to target more they are at a loss to anticipate changes in than one million customers. The full launch these needs. This was once the case for a large included four weeks of coaching for more European bank operating in diverse regions. It than 600 relationship managers. Within five now draws on a broad range of data to under - months of starting the project, the NPtB was stand general market trends and specific com - fully up and running, with the predictive pany behaviors. This requires not only model stabilized and relationship managers applying advanced analytics to traditional fully trained. Ultimately, this bank increased types of information (annual reports, market new sales by more than 30 percent, and rela - conditions, competitor news) but also collect - tionship managers increased their interac - ing publicly available data on social media tions with commercial customers by more (company-managed pages, customer com - than 50 percent. ments, etc.). An NPtB engine extracts insights The pilot is an important opportunity to secure from available data to alert corporate treasur - the endorsement of team members participat - ers to new opportunities, for example, to lever - ing in the pilot, who then share information age complex banking relationships to improve about the model with other colleagues. The cash flow and lower the cost of short-term fi - pilot is also an opportunity to test metrics for nancing. Identified leads include opportunities

Using data to unlock the potential of an SME and mid-corporate franchise 34 in various currencies, possibly triggering a ship managers, service representatives, change in cross-border pooling arrangements; and product specialists to help customers letters of credit, domestic and international weigh their options and choose the path guarantees; or even investment banking prod - that best serves the company’s financial in - ucts, e.g., debt capital management. NPtB en - terests. gines can boost new sales among large 3. Pilot the outcome of the NPtB engine to corporate clients by as much as 15 percent. build confidence and secure buy-in. Re - Implementing NPtB for SMEs/mid- lationship managers must be confident in corporates the opportunities identified by the NPtB The lessons learned from banks that have im - engine; at the end of the process they will plemented an NPtB engine can be summa - leverage leads to improve their sales effec - rized in five points: tiveness, but change management and in - 1. Design the NPtB engine according to ternal buy-in are key for successful the characteristics of the market seg - implementation. ments served. Consider first internal data, 4. Focus on prototypes that create excite - including company profiles, relationship ment. Don’t let IT and the complexity of characteristics, product granularity, and legacy systems become the bottleneck, but opportunities. Expand the data set to in - start with a pragmatic “proof-of-concept” clude external data, testing the relevance to demonstrate the model’s potential. of the new variables in generating useful Quick test-and-learn prototypes have mul - recommendations. In developing algo - tiple purposes, including learning and im - rithms to generate predictions for the large proving but above all showing prompt corporate segment, it is important to test a impact to create enthusiasm. broad variety of external data in order to 5. Ensure impact from multiple levers. build a robust data set that can produce in - Better targeting based on analytics is cru - sights with a new level of accuracy. cial, but there are additional levers, includ - 2. Build the model around customer ing the timing of recommendations, needs and interests. One of the biggest framing recommendations within a impacts is shifting from a “product push” broader value proposition, measuring the approach to interactions that address spe - impact of recommendations (including the cific customer needs, as reflected in cur - performance of relationship managers), rent transaction activity and financial which can also improve performance. performance. This shift enables relation -

Ignacio Crespo is a partner in McKinsey’s Madrid office, where Carlos Fernandez Naveira is an expert associate partner. Huw Kwon is a senior analytics expert in the London office.

35 McKinsey on Payments August 2018 Hidden figures: The quiet discipline of managing people using data

The “war for talent” in financial services has evolved to encompass new frontiers and unfamiliar battlefields. This evolution is fueled by the fundamental transformation of capabilities essential to payments organizations and more broadly, banks’ future success: skills in digital technology, artificial intelligence, and automation alongside less tangible abilities such as problem-solving, emotional intelligence, resilience, and adaptability. Similar transformations are playing out across sectors however, leaving such capabilities in scarce supply. At the same time, banks’ historical playbook for attracting, developing, and retaining talent is in need of update given erosion in the perceived advantages of a banking career relative to other sectors.

Carla Arellano Successfully addressing this challenge pays development (e.g., scrum master), analytics Vijay D’Silva dividends. Banks that prevail in the renewed (e.g., data scientists), new risk management Malte Gabriel war for talent will place greater emphasis on roles (e.g., cybersecurity analysts), and digital Jaime Potter employee attraction, management, develop - marketing (e.g., UX designer). However, re - ment and retention, starting with revamping search from technology-sourcing firm Cata - their employee value proposition and em - lant finds that while many companies have bracing evidence-based talent management begun to address top technology and training by deploying new capabilities such as data, challenges, most continue to rely on tradi - analytics, and organizational science. We be - tional recruiting models that show signs of lieve that banks and payments companies erosion, leading to key roles often taking 90 that recognize their talent management op - or more days to fill. By 2021, McKinsey proj - portunities, set bold aspirations, and embrace ects that demand for talent with digital capa - new capabilities to address these imperatives bilities will outstrip supply by a factor of four will increasingly distance themselves from in areas like agile, and by 50 to 60 percent for their competition. big-data talent, according to a study con - ducted two years ago. The new war for talent The renewed war for talent is global, urgent McKinsey framed the war for talent as a and poses daunting challenges, for which the strategic business challenge in 1997, setting imperative is not only to attract and retain forth the notion that better talent leads to the highest performers, but also to enable better corporate performance. Bank leaders leaders to better manage talent to deliver sus - embraced the concept of talent as their firm’s tainable competitive advantage. A 2018 McK - most valuable asset; however, responsibility insey analysis of European financial services for hiring and development continued to be firms on the future of work identified the delegated to human resources or line man - critical talent segments these institutions agers. C-suite leaders focused on other prior - need to fill over the coming years. Top of this ities while battles for talent were won with list are new roles in software and application monetary incentive packages that tech firms

Hidden figures: The quiet discipline of managing people using data 36 and companies in other sectors were unable similar equity incentives made the tech field to match. more lucrative, shifting the playing field just as banks trimmed their post-financial crisis In the years following the financial crisis, bonuses. What once was a bank EVP selling banks focused on cost reduction and risk point has now faded in comparison to other management to battle margin and regulatory industries. pressures. This left a blind spot for strategic talent questions, and many banks now find It’s not only rigorous technical skills that are themselves with a significant gap in their gaining importance for banks. An ironic as - perceived employee value proposition (EVP) pect of the shift towards automation of many compared to technology and other leading 20th-century jobs is the increasing focus on sectors (Exhibits 1,2). As a result, future people skills—flexibility, problem-solving leadership talent is turning away from ca - under uncertainty, collaboration, and emo - reers in finance. In 2007, four times as many tional intelligence, to name a few. While US MBA graduates chose to enter the fi - these soft skills often get short shrift when nance field over technology. By 2017, these sized up against measurable technical skills two groups were roughly at parity. IPOs and in Python and deep learning, their value

Exhibit 1 Technology has overtaken banking in perceived attractiveness of compensation and beneﬁts over the last 4 years.

Employee sentiment about compensation Improvement/Decline and beneﬁts in rating 1 is lowest and 5 highest rating

3.82

-1.8% Banking1 3.78

3.75 3.6% Technology2

3.65

2014 2018

1 Nine largest US banks by 2017 assets 2 Ten largest US tech Þrms by 2017 revenue Source: McKinsey analysis of publicly available data

37 McKinsey on Payments August 2018 should not be underestimated. A University rative approaches of effective teams. En - of Michigan study showed that investing in gagement surveys—with greater depth and training of soft skills yielded a whopping 250 quicker turnaround than in the past—offer percent return on investment in certain in - valuable insights into the themes and sen - stances. timents diverting employee focus, and em - ployee location data can improve We have reason to believe that the war for tal - operational efficiencies in sectors such as ent is here to stay. Several parallel forces have restaurants and delivery services. fundamentally altered the global landscape, altering banks’ roadmap to victory in the tal - Imperatives for speed and accuracy: In ent management space: an uncertain competitive environment marked by ever-shorter technology lifes - Massive amounts of workforce data: pans, winners in the war for talent will be The explosion of data over recent years— able to quickly identify talent gaps at a combined with the power to store and dis - micro-granular level. Agile talent manage - sect it—opens new avenues for talent ment will drastically reduce the cost asso - management. Email and calendar data are ciated with having the wrong or even no now being used to benchmark the collabo - talent at all in critical roles.

Exhibit 2 Banks are perceived as more stressful and competitive than technology and other sectors.

Banks’ culture is ...1

Technology1 90% “slow- 0% 100% “quick- paced” paced” Banking 91% 94% Other corporates

Technology1 62% “stress- “relaxed” 0% 100% ful” 65% 73% Banking Other corporates

Technology1 50% “collabo- “compe- 0% 100% rative” titive” 51% 53% Banking Other corporates

1 Analysis on employee reviews from 2017-18 for 29 banks, corporate MNCs, and tech Þrms: largest US banks by assets 2017; largest US tech Þrms by revenue; top 10 non-Þnancial, non-consulting, non-tech Þrms sought by MBAs. Source: McKinsey analysis

Hidden figures: The quiet discipline of managing people using data 38 Shortage of talent and a rapidly evolv - Winners will be those firms who can harness ing workforce: Forty-six percent of em - data, advanced analytics, and behavioral sci - ployers have difficulty filling jobs, mainly ence to make sound people and organization due to a shortage of applicants. Finance decisions faster, better, and with a level of staff, a core constituency of banks and pay - specificity previously unavailable. This will ments firms, rose to the sixth-most-diffi - enable them to preserve advantages gained by cult job category to fill in 2016, up from better deploying and nurturing skills across ninth in 2015. Winners will be able to fill the full talent lifecycle. jobs quicker and with better people as mi - We see three areas in which firms must mas - crotargeting and evidence-based selection ter data and analytics in order to win the war allow firms to identify and tailor hiring to for talent: target talent and assess candidates more consistently to hire high performers. Assess talent gaps and address accord - ingly: The adage “culture eats strategy for Many banks have taken note of how talent is breakfast” may soon be replaced—or at impacted by these trends. An analysis of earn - least complemented with—“and capabili - ings call transcripts of the ten largest US banks ties take lunch.” While many organizations reveals that talent-related terms are being used invest significantly in strategy, the key is three to four times as often in recent quarters securing the capabilities needed to deliver than they were in the 2012-15 period. With em - that strategy. Leveraging internal and ployee turnover rates at ten-year highs, CEOs third-party data allow firms to quantify or - need to find ways to not only secure talent for ganizational skills deficits, target opportu - the future, but also to stem near-term attrition nities for re-skilling (through methods and its drag on profitability. such as hierarchical clustering or cosine Winning with analytics similarity), and identify the skills to source We have documented substantial perform - externally. A 2017 McKinsey Global Insti - ance differences between the leaders and tute report on automation, employment laggards in this new war for talent. Research and productivity showed that 43 percent of outlined in the book Talent Wins, co-au - all finance and insurance activities can be thored by outgoing McKinsey Managing automated through currently available Partner Dominic Barton, demonstrates that technology. The aforementioned McKinsey companies that use data and advanced ana - study on the future of work in financial in - lytics to inform their talent decisions realize stitutions found that one-third of existing up to a 30 percent increase in profits talent gaps can be addressed by re-skilling through hiring focus alone—before account - current employees. One client established ing for the benefits of higher productivity a best-practice adult-learning program, and better retention. Furthermore, 2018 combining both in-house and external McKinsey research on performance manage - learning, and retrained more than 1,000 ment indicates that organizations with ef - employees into new internet of things, an - fective performance management are 77 alytics, and machine learning roles within percent more likely to outperform competi - the first ten months of the program. Win - tors and peers. ners in automation transformations pin -

39 McKinsey on Payments August 2018 point capability requirements and make to reduce bias and enable algorithmic the proper call on where to buy (source), analysis of answers. Unilever uses tech - build (re-skill), and rent (outsource or shift nology across its whole recruiting to contractors). process; it begins by using LinkedIn pro - files instead of résumés, deploying AI to Attracting and retaining the best tal - select the best prospects. Next, it uses a ent: Microtargeting allows a firm to tailor series of online games to further narrow its EVP and its communication to critical the field to a select few candidates for in- talent segments to increase conversion person interviews. The results are con - rates. There is clear evidence that objec - vincing: Using this approach Unilever tive hiring powered by analytics and be - tripled the roster of universities from havioral science (versus traditional which it recruits while reducing its aver - interviews) leads to better hiring deci - age hiring cycle from four months to four sions and greater value creation (Exhibit weeks. McKinsey’s own use of artificial in - 3). In this area banks can learn from each telligence to screen résumés not only de - other, as well as from other sectors. For livered 30 to 50 percent increases in instance, firms like Aegis Worldwide con - hiring efficiency (reflecting a 400 to 500 duct text message-based initial interviews

Exhibit 3 Bank x is missing data science capabilities, and can address the gap by tapping into existing backend database and programming expertise.

Skills Gap Analysis Adjacency Analysis

Skills Category Target Company Skills Category Target Company Data Science Data Science

Skills Category Adjacency

spss Undersupply 20% vis a vis market skills category 18% quantltative Þnancial Backend database r Oversupply 16% data-mining vis a vis market 14% segmentation

1 12% y

predictive analytics t i l i

data science b a 10% l i

mathematics a Backend programming v A text mining 8% quantltative analytics 6% sas Ul Technology statistical modellng 4% stata 2% machine learning Ul Design 0% statistics 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15% 16% 17% 18% 19% 20% 21% 22% 23% 24% 25%

matlab Adjacency2 -30.0% -20.0% -10.0% 0.0% 10.0% 20.0% 30.0% 1 Availability: % of employees having at least one skill within the selected skills category (within the target company) 2Adjacency: Availability in total population/ Availability in population wlth selected skills. Metric Is normalized: highest value= 100%

Source: McKinsey analysis

Hidden figures: The quiet discipline of managing people using data 40 percent ROI), but also drove an increase High performers are often at the highest risk in the share of women candidates passing of attrition, given their multitude of outside initial screening. options. Data and analytics can serve as an early warning and mitigation system by pre - One financial services firm found it could in - dicting attrition risk at both individual and crease field effectiveness by systematically group levels and developing effective re - testing and recruiting for character traits sponses to address the root cause. For in - such as curiosity and de-emphasizing humil - stance, using k-medoid and majority vote ity, which was found to be underrepresented classification techniques, one financial institu - among its high performers. Another applied a tion found that attrition was elevated among predictive modeling technique called elastic three different groups—millennials seeking net regularization to hire more employees professional growth, employees working in with character traits similar to those of exist - larger teams, and those working for lowing high performers, reallocating recruiting tenured managers. Leveraging this data-dri - dollars to particular schools and majors and ven employee segmentation, the organization disintermediating headhunters in approach - developed tailored preventive measures to re - ing high-potential candidates. A fast-food duce attrition for each of the clusters. chain collected data on employees’ character traits and behaviors in the workplace and Better manage and deploy talent generated more targeted recruiting, roles and A plan to grow and deploy talent starts with expectations, culture-focused trainings, up - identification of what drives true perform - dated financial and non-financial incentives, ance—collecting data to create a 360-degree and optimized shifts. The company soon doc - view of who your employees are, what they do, umented customer satisfaction gains of 130 who they interact with, how they’re de - percent across stores, 30-second speed-of- ployed—linking this information to the rele - service improvements, and per-store revenue vant dependent variables and building increases of 5 percent. optimization strategies. This typically starts with a data-driven assessment of the organiza - We predict that winners will go beyond de - tional context for employee performance. For ploying “off the shelf” assessments to de - instance, to what extent does a manager’s span velop evidence-based models supporting the of control impact individual performance? knowledge, skills, attributes, and experi - What role does coaching play in performance? ences required to successfully deliver on a More ambitious initiatives might develop specific role in its unique environment. This guidance on time allocation, collaboration pat - can be accomplished through closed-loop terns, meeting practices, and more, through machine learning to pinpoint what factors behavioral data such as calendar and email distinguish high performers from the rest, or metadata (with appropriate encryption science-based forensics on future work re - methodologies to maintain employee privacy). quired, which informs objective screening criteria to be assessed through science- As an example, one financial institution built backed interviews and digital assessments, and analyzed a behavioral dataset of how gamified or otherwise. leaders split their time across recruiting,

41 McKinsey on Payments August 2018 coaching, clients, and other activities to iden - thousands of employees with unique values, tify a 30 percent growth opportunity in in - goals, and aspirations working at modern-day vestments. The bank reallocated leaders’ time organizations. from administrative and controls-oriented While many people-related changes take tasks to customer-centric coaching and fos - time to reach full potency, most organiza - tering connectivity across lines of business. tions possess the building blocks in both ca - Another firm leveraged McKinsey’s data-dri - pabilities and data to start with small ven Talent to Value approach to identify a se - changes today to pave the way for larger lect number of critical roles driving the most shifts tomorrow. A key first step is to identify economic value—some extending as far as the human component of business chal - four levels below the CEO. This client discov - lenges and opportunities, and build an ana - ered that closer collaboration across four of lytics engine to collect data and validate 30 critical roles was critical to delivering hypotheses on performance drivers. For ex - more than 50 percent of the value at stake. ample, in a corporate bank, analytics on cal - Rather than merely encouraging general col - endar meta data may help pinpoint the laboration across the enterprise, the firm interaction patterns related to deal success. doubled down on tactical incentives for col - While in payments, data and analytics can laboration among these roles by, for example, enable faster and more nuanced hiring of the implementing shared goals, with 30 to 50 right combination of technical and “soft” percent of leaders’ KPIs driven by factors be - skills. Deploying analytics to create trans - yond their direct control. parency into what matters—for leaders, man - These steps create a “virtuous cycle” bene - agers, and employees—empowers them to cut fitting workers as well as employers. A better through the noise and focus on what really selection process leads to better organiza - matters. Financial institutions looking to up - tional fits, which in turn fosters employee grade their talent management practices can satisfaction and enhanced EVP. When organ - follow a few simple guidelines to get started: izations are redesigned to be more collabo - Make talent the business’s agenda: A rative and agile, not only does employee firm’s people analytics agenda must focus time allocation change, but roles evolve too on critical business needs and originate and traditional management hierarchies be - from a strong hypothesis on which factors come redundant. At the end of the process, do and do not matter to business perform - one European bank eliminated two entire ance. Setting this agenda is a collaboration layers of middle management while its em - between business and HR leaders. ployee engagement scores rose by over 20 percentage points. Don’t underestimate what you already have: Relevant data is often already avail - What can be done today? able and can be complemented with nomi - Humans do not change their behavior with nal effort. In our experience, three out of the flip of a switch. It may take years to get a four banks already possess the necessary single individual to change behavior, which is data—such as attrition rates, team struc - only compounded when we consider the tures, employee backgrounds, and average

Hidden figures: The quiet discipline of managing people using data 42 time to fill a position—to test the most most new employees were not being hired pressing people analytics hypotheses. into the most critical divisions and roles, and that base pay was more correlated with Treat data with the care and rigor it de - age and job grade than criticality of the serves: Protecting data privacy and em - role or performance. The people analytics ployee confidentiality are critical journey is a transformation, comparable to objectives, not only since the GDPR rules robotic process automation. Start small, on data protection took effect earlier this adhere to high standards when handling year. Protecting data privacy is also core to data, and quickly prove the value of the ap - preventing a situation where employees proach. A test-and-learn approach makes a feel they are being unduly monitored or difference, running trials to prove business even manipulated. Create transparency on value before scaling more broadly. which data is sourced, how it is used and the tangible benefit that people analytics * * * can provide. Establish protocols and en - Twenty years ago, the war for talent was cryption policies to appropriately fought with major changes in employee envi - anonymize and mask information. ronment and compensation systems, trigger - Start small and build over time: Signifi - ing a number of innovations and a new cant value can be gained by combining HR, informality—down to casual Fridays. Today’s financial, and operational data for basic changes are more nuanced and targeted. In - “talent due diligence.” The first step is to stead of large-scale changes, the new war for identify drivers of compensation growth, talent will likely involve thousands of subtler performance ratings, promotions, and vari - microdecisions. This scope can seem daunt - ance across the organization. This will ing. Fortunately, embedding data and analyt - begin to infuse talent decisions with the ics into an organization’s people function rigor normally reserved for financial deci - begins with a few simple changes today that sions. By running a talent due diligence, will lay the groundwork for a more profitable one European bank quickly identified an organization and more fulfilled employees. opportunity for retooling, finding that

Carla Arellano is a partner in McKinsey’s New York office, where Vijay D’Silva is a senior partner. Malte Gabriel is a data science specialist in the North America Knowledge Center, and Jaime Potter is a senior product analyst in the New Jersey office.

43 McKinsey on Payments August 2018 Using analytics to increase satisfaction, efficiency, and revenue in customer service

As payments providers around the globe cope with increasing pressure on revenues and margins, customer service is increasingly becoming an important asset for driving top- and bottom-line performance, and improving the customer experience. While most banks, card companies, and other payments providers have implemented various degrees of customer service transformation by using advanced analytics, the discipline has yet to be fully leveraged in this regard. To realize the full potential of today’s analytical capabilities financial institutions will need to possess, acquire, or develop the relevant capabilities and use them to customize and enhance a wide range of customer interactions.

Krishna Bhattacharya Payments providers that adopt advanced ana - enjoy at other financial institutions and Greg Phalin lytics to develop broad integrated approaches leading service providers, like Amazon and Abhilash Sridharan are seeing significant improvement: cus - Zappos. To deliver this level of service, tomer satisfaction scores rose 5 to 10 percent payments firms need to optimize customer and operating costs declined 15 to 20 percent and prospect telecommunications and deliver when they used analytics to eliminate cross- seamless omnichannel interactions. channel leakage and migrate more customer Building an omnichannel customer service interactions into self-serve channels. Analyt - model ics also enabled these firms to improve cus - Traditionally, financial institutions have tomer retention and revenues by 10 percent tried to optimize customer service within or more, by enhancing the customer journey channel silos, including call centers, online, and improving cross-selling. and mobile. The key to delivering a high- The future of customer service quality omnichannel experience is adopting a Customer service is shifting dramatically, broad customer journey approach that from phone and branch-centric models to an integrates customer interactions across omnichannel interaction dynamic in which digital and traditional channels. Several customers move seamlessly among service institutions have already embarked on such a channels, including mobile, phone, chat, and model. A global life insurer, for example, online. A McKinsey survey in 2015 showed recently developed a five-year plan to migrate digital channels accounted for 30 percent of nearly half of its customer journeys into self- customer interactions. We expect this share serve channels. However, too often such will approach 50 percent by 2020. And of this, changes are viewed as one-time efforts rather 26 percent will be exclusively digital with no than as a large-scale transformation. branch interaction. Designing a comprehensive, ongoing program is key to sustaining omnichannel service Payments customers expect high-quality improvements. service across channels, similar to what they

Using analytics to increase satisfaction, efficiency, and revenue in customer service 44 Investing in the talent to transform Financial institutions that are successfully A key part of transforming the customer ex - using advanced analytics to enhance the cus - perience is migrating basic transactions to tomer experience share six common hall - self-service channels, and complex transac - marks (Exhibit 1). tions to agent-assisted channels. While most 1. Migrating customers to digital channels organizations invest in ongoing agent train - Given customers’ preference for omnichan - ing and capability building, transforming the nel service, there are two important ques - customer experience demands a more sub - tions financial institutions must address: stantial investment in talent. It requires in - First, how do they create seamless transac - vesting in technology that enables customer tions for digital natives, who prefer digital- service professionals to have more effective only service? Second, in serving less digitally interactions with customers. For example: inclined customers, how can financial institu - • Real-time coaching software, such as Cog - tions use tools like journey analytics to pre - ito, provides live feedback about customers vent the use of multiple channels for the to agents during customer calls, so agents same query? The main challenge for cus - can tailor the discussion to customer needs. tomer service organizations is to identify the most appropriate transactions for migration • Applications such as Verint use speech an - and ensuring they are completed satisfacto - alytics that foster more personalized inter - rily in digital channels whenever possible. actions with customers. Payments leaders in digital migration are To provide more personalized customer serv - achieving 20 to 30 percent reductions in call ice, financial institutions must rethink how volume and successfully enhancing the cus - they interact with customers and prospects. tomer experience. Some industry leaders are Analytics can personalize customer experi - also developing a 360-degree, multitouch, ence by, for example, identifying the next- multichannel view of customer interactions best action or product offering. (See "Using using journey analytics; but this requires ro - data to unlock the potential of an SME and bust integrated datasets that can capture cus - mid-corporate franchise," page 28.) tomer interactions across channels.

Investments in technology are, of course, 2. Improving behavioral routing and IVR critical to transforming the customer experi - containment ence. Two investment types in particular are Financial institutions have been using inter - key: developing the agility to rapidly build, active voice response (IVR) technology for pilot, and launch a broad transformation; and several decades, but few have optimized these robotics or artificial intelligence (AI) to re - capabilities. Doing so requires more than in - duce manual workloads, improve cycle times, vesting in additional VR capabilities. Finan - and minimize back office errors. McKinsey cial institutions can apply advanced analytics research shows that 65 percent of back-office or AI-based technologies to improve behav - tasks at contact centers, and 30 to 50 percent ioral routing and IVR containment: of front-line calls, can now be automated. • Using analytics to identify reasons for call Six hallmarks of analytics success transfers can help increase the number of

45 McKinsey on Payments August 2018 interactions contained within the IVR envi - • Directing calls from high-potential cus - ronment. Deeper analysis of calls can clas - tomers to agents trained to present tailored sify customers into clusters based on value, products (using algorithms based on the behavior, and tenure, speeding up IVR serv - customer’s needs) can boost productivity. ice and streamlining unnecessary trees. 3. Strengthen identity validation and • Matching agents to callers based on per - personalize product offerings sonality (using technologies like Afiniti and The layering of analytics on video and audio Mattersight) can meaningfully improve channels can improve identity validation and customer experience and call efficiency. personalize the product offering. Examples include:

Exhibit 1 Analytics use cases in customer care center around six core imperatives.

13 Incorporate people analytics into talent 1 Achieve 20-30% reduction in call volume and management processes enhance customer experience by targeting right customers for digital migration 12 Empower front line to serve customers with real-time performance support 2 Develop multitouch, multichannel view of customer interactions using journey analytics

A Migrate customers to 3 Enhance customer digital channels experience by matching 11 Develop capabili- F and enable B agents to callers based ties to minimize adoption on reason/personality Use manual tasks and using AI technology Optimize behavioral deliver time and routing and IVR cost savings frontline 4 Optimize agent utiliza- performance containment to enhance tion via better demand 10 Enable automated customer forecasting and supply service delivery Delight experience optimization, driven by and adaptive customers at advanced analytics learning through a E attractive costs C cognitive 5 Contain IVR calls by response system Use Improve analyzing reasons for automation to identity call transfers and improve employee validation and building relevant capa- efÞciency and D personalize bilities engagement customer Optimize offering 6 Optimize call center workforce routing (via advanced management analytics algorithms) model 9 Use analytics based on household center of excel- potential and next best lence with tools, offer functionality benchmarking and expertise to identify and 7 Replicate face-to-face interactions in a remote capture work- environment using optimization software force optimization opportunities 8 Use video and audio analytics solutions to simplify and improve identity validation

Source: McKinsey Analytics

Using analytics to increase satisfaction, efficiency, and revenue in customer service 46 • Replicating face-to-face interactions in a mated knowledge management systems) and remote environment using optimization back-end automation (e.g., robotic process au - software enables more personalized and tomation). Virtual agents can solve customer secure interaction. requests by using natural language processing technology, and get smarter over time through • Identity validation can be simplified and machine learning. For example, programs like improved with features like facial recogni - IPSoft’s Amelia can play the role of any cus - tion (online identification) and voice tomer service agent by rapidly absorbing call recognition (in app account access). logs, recognizing emotional context, and inter - 4. Optimize the workforce management acting with customers, thereby saving costs model and lifting both revenue and customer experi - Most financial institutions have established ence. With large tech players moving into the internal analytics centers staffed with ex - digital assistant arena, we expect things to perts working to capture workforce optimiza - evolve quickly in this area. tion opportunities. Yet, most workforce management practices are rooted in back - 6. Optimize frontline performance through analytics in recruiting ward-looking general demand-supply match - Recruiting processes for customer service or - ing, assuming some average service level for a ganizations are seldom informed by what day. However, customer research reveals that makes agents successful. Leading firms take assumptions of averages fall short. There are an approach called people analytics method - three important challenges for each financial ology, which reverse engineers the process, institution: starting with the best customer service agents • How can they effectively manage the tails and identifying common traits that makes that drive customer satisfaction or dissat - them successful. They then apply these in - isfaction? sights at the top of the recruiting funnel in se - • How can they use machine learning to lecting candidates. By applying people manage resiliency and drive the next level analytics in this way, financial institutions can of predictive modeling on demand (e.g., improve talent management in customer ex - impact of hurricanes)? perience as well as in the wider organization.

• How can analytics centers use real-time Case example I: Improving digital channel experience and digital adoption simulation tools to create efficiencies in Recently, a North American bank used jour - workforce management? ney analytics to accelerate digital adoption 5. Automate to improve employee efficiency across its customer base. Using analytics and and engagement design thinking to address digital adoption Thus far, automation has not been systemati - levers across customer journeys (rapid digiti - cally applied in the customer service environ - zation, containment, signature moments, ment. In customer care, AI can be used to customer targeting), the bank achieved a gain automate services by supporting customers of more than 20 percentage points in digital with virtual agents, and contact center agents engagement. The initiative included the fol - through real-time interaction tools (e.g., auto - lowing elements:

47 McKinsey on Payments August 2018 • Journey level scan: Using interaction and delivered a differentiated experience data and analytics from all channels (digi - along the customer journey: tal, call, branch, email/text, ATM), the 1. To better understand its customers’ behav - bank prioritized about 15 core customer ior, the company analyzed five million cus - journeys and more than 40 sub-journeys tomer calls. With these findings, they for digitization, classified customers into eight archetypes • Quantified journey redesign: The bank based their value, behavior, and length of then redesigned each core journey using time as customers. analytics-based Quantified Experience De - 2. Management also used brainstorming sign (QED), 1 leading to an increase in digi - techniques to develop and refine several tal engagement of 10 to 15 percentage initiatives based on feasibility, potential points, and similar improvements in cus - economic impact, and customer experi - tomer experience measures. Analytics ence improvement. This generated 48 pri - drills targeted key drivers of customer ex - oritized initiatives that spanned VR (e.g., perience and other cross-cutting themes. capture additional information and make • Real-time customer nudging: The bank it less easy for customers to “rep out”), introduced a customer targeting process routing (e.g., adapt service standards to based on customer behaviors and journeys match expectations of different cus - to accelerate digital adoption, which gen - tomers), and post-VR (e.g., focus on edu - erated a 5- to 10-percentage-point increase cation and self-service awareness for in product adoption disengaged customers).

• Journey tracking: The bank transitioned 3. The company also surveyed 1,500 employ - from an overall customer experience- ees, conducted focus groups that engaged based performance measurement system managers, and surveyed more than 1,000 to one based on operating drivers for each customers to explore tactics for increasing journey and channel, to track improve - IVR containment and digital engagement. ments and re-orient program Through these efforts, the credit card • Capability building: Using journey ana - provider identified 200 to 500 bps in poten - lytics and QED, the bank designed and tial improvement in the containment rate launched a capability-building program for (Exhibit 2). The VR enhancements and post- more than 800 contact center agents. VR agent initiatives also led to a 5 to 10 per - cent reduction in costs or incremental Case example II: Enhanced contact annualized savings. management A credit card company was struggling to mi - Case example III: Demand forecasting 1 QED is a data and design methodology The call center head of a large UK-based bank that helps executives prioritize and grate customers to its self-serve channels de - implement customer journey designs, spite having invested in natural-language turned to analytics to optimize agent utiliza - and helps designers focus on features that will maximize value for customers. speech IVR. Consequently, it devised a three- tion by automating demand forecasting, as For more insight visit: www.mckinsey.com/business- pronged approach to accelerate migration, part of a larger analytics-driven transforma - functions/digital-mckinsey/our-insigh tion at the institution. The approach incorpo - ts/digital-blog/design-meet-data- which focused on resolving (and containing) unlocking-design-roi. a higher percentage of calls within their IVR, rated the following elements:

Using analytics to increase satisfaction, efficiency, and revenue in customer service 48 Exhibit 2 VR containment and differentiated customer experience led to 200-500 bps containment increases and annual cost savings of 5-10%.

Containment potential %

100 5 +200-500 BPS 25 15 5 50 2-4 0-1 52-55

All calls Forced Disengaged No Policy Current VR Post VR Target VR transfer customers functionality kick-outs containment enhancement treatment containment

Saving potential %

100 3-5 ~5-10 1-2 1-3

95-90

Baseline VR Differentiated Post VR Future IVR cost enhancements experience VR ‘agent IVR cost enhancements initiatives’

NOTE: 100M calls into the client VR per year Source: McKinsey Analytics

• Creation of a robust integrated dataset number of calls and average handling time, that is foundational for the analytics exer - on a monthly basis (4 to 16 months ahead cise, by combining five different data and updated monthly) and a 30-minute sources—data for more than ten million level basis (8 to 10 weeks ahead and up - customers, call data, agent data, bank data dated daily) related to IT outages, and other external • Bayesian techniques to capture most re - data (e.g., weather) cent dynamics for extrapolation, non-lin - • Development of two sets of random for - ear regression models for forecasting, and est machine learning models to continu - more than a hundred features to capture ously learn thresholds and forecast both different levels of seasonality.

49 McKinsey on Payments August 2018 The bank achieved a 20 to 40 percent error transformation by building a business case, reduction in forecasting for a subset of popu - educating their leadership, and obtaining or - lation and are rolling it out across all FTEs. ganizational buy-in. Once these initiatives are underway, quick, tangible wins should be Starting the journey on analytics to customer service pursued to reinforce the organization’s com - When introducing advanced analytics, a criti - mitment to a full transformation. Addition - cal first step is clearly understanding the or - ally, another challenge faced by these ganization’s current position in terms of one organizations is lack of in-house knowledge of three horizons (Exhibit 3): on relevant frameworks and solutions, to di - agnose and prioritize initiatives. Those on Horizon 1 generally have low levels of awareness regarding recent developments Enterprises at Horizon 2 have a better un - in advanced analytics for customer service. derstanding of recent advances in the field, These organizations need to begin their and have started to experiment with or

Exhibit 3 Most ﬁnancial institutions are situated on one of three horizons in terms of their customer service analytics journey.

Horizon 1 Horizon 2 Horizon 3

Financial institutions Financial institutions that Firms that are already in mostly in the preliminary have an analytics plan the midst of next-gen stages of digital and but an unstructured digital and analytics analytics evolution approach, leaving value transformation, but need on the table support to accelerate value delivery

Typical Need to build awareness Lack structured roadmap, Business-as-usual challenges among senior leaders benchmarked with activities crowd out about analytics use external best practices, dedicated resourcing to cases and develop buy-in to drive milestones implement the project to start transformation Lack well articulated plan Lack knowledge about Lack of framework and for how to create and best practices in relevant solutions to capture value analytics solutions diagnose and prioritize Lack capabilities, tools across industries, to help initiatives and talent to execute set benchmarks and Need early tangible wins accelerate value delivery to reinforce senior leadership’s conÞdence in transformation

Source: McKinsey Analytics

Using analytics to increase satisfaction, efficiency, and revenue in customer service 50 adopt them. However, they have done so to advance to even higher levels, and to con - largely on an ad hoc, unstructured basis. Un - tinue to invest in next-generation solutions. fortunately, informal approaches are likely * * * to leave significant value on the table. The key challenge for Horizon 2 organizations is The use of new analytical tools and capabili - to identify the most efficient path for deliv - ties are transforming customer service in fi - ering the desired results. This might be ac - nancial services. The following questions can complished, for instance, by shaping their help firms shape their strategy discussions: perspectives through a sharing of external • Where do we stand currently in terms of best practices, and then setting challenging the three advanced analytics/customer timelines. service horizons? Horizon 3 firms are well ahead of the curve, • What challenges are preventing us from applying next-generation analytics solutions advancing to the next horizon? to transform the customer service model. At this stage, the key challenge is finding ways • What immediate steps can we take to ad - dress these challenges?

Krishna Bhattacharya is a partner, Greg Phalin is a senior partner, and Abhilash Sridharan is a consultant, all in McKinsey’s New York office.

51 McKinsey on Payments August 2018 Designing a data transformation that delivers value right from the start

The CEOs of most financial institutions have had data on their agenda for at least a decade. However, the explosion in data availability over the past few years—coupled with the dramatic fall in storage and processing costs and an increasing regulatory focus on data quality, policy, governance, models, aggregation, metrics, reporting, and monitoring—has prompted a change in focus. Most financial institutions are now engaged in transformation programs designed to reshape their business models by harnessing the immense potential of data.

Chiara Brocchi Leading financial institutions that once used with some seeing scant returns from invest - Davide Grande descriptive analytics to inform decision- ments totaling hundreds of millions of dollars. making are now embedding analytics in Kayvaun Rowshankish A 2016 global McKinsey survey found that a products, processes, services, and multiple Tamim Saleh number of common obstacles are holding fi - front-line activities. And where they once Allen Weinberg nancial institutions back: a lack of front-office built relational data warehouses to store controls that leads to poor data input and lim - structured data from specific sources, they ited validation; inefficient data architecture are now operating data lakes with large-scale with multiple legacy IT systems; a lack of busi - distributed file systems that capture, store, ness support for the value of a data transforma - and instantly update structured and unstruc - tion; and a lack of attention at executive level tured data from a vast range of sources to that prevents the organization committing it - support faster and easier data access. At the self fully (Exhibit 1). To tackle these obstacles, same time, they are taking advantage of smart institutions follow a systematic five-step cloud technology to make their business process to data transformation. more agile and innovative, and their opera - tions leaner and more efficient. Many have 1. Define a clear data strategy set up a new unit under a chief data officer to Obvious though this step may seem, only run their data transformation and ensure about 30 percent of the banks in our survey disciplined data governance. had a data strategy in place. Others had em - barked on ambitious programs to develop a Successful data transformations can yield new enterprise data warehouse or data lake enormous benefits. One US bank expects to see without an explicit data strategy, with pre - more than $400 million in savings from ration - dictably disappointing results. Any success - alizing its IT data assets and $2 billion in gains ful data transformation begins by setting a from additional revenues, lower capital re - clear ambition for the value it expects to quirements, and operational efficiencies. An - create. other institution expects to grow its bottom line by 25 percent in target segments and prod - In setting this ambition, institutions should ucts thanks to data-driven business initiatives. take note of the scale of improvement other Yet many other organizations are struggling to organizations have achieved. In our experi - capture real value from their data programs, ence, most of the value of a data transforma -

Designing a data transformation that delivers value right from the start 52 tion flows from improved regulatory compli - Actions: Define the guiding vision for your ance, lower costs, and higher revenues. Reduc - data transformation journey; design a strategy ing the time it takes to respond to data to transform the organization; establish clear requests from the supervisor can generate and measurable milestones. cost savings in the order of 30 to 40 percent, 2. Translate the data strategy into tangible for instance. Organizations that simplify their use cases data architecture, minimize data fragmenta - Identifying use cases that create value for the tion, and decommission redundant systems business is key to getting everyone in the or - can reduce their IT costs and investments by ganization aligned behind and committed to 20 to 30 percent. Banks that have captured the transformation journey. This process typ - benefits across risk, costs, and revenues have ically comprises four steps. been able to boost their bottom line by 15 to 20 percent. However, the greatest value is un - In the first step, the institution breaks down locked when a bank uses its data transforma - its data strategy into the main goals it wants tion to transform its entire business model to achieve, both as a whole and within indi - and become a data-driven digital bank. vidual functions and businesses.

Exhibit 1 Typical challenges in data transformations at banks.

Challenges faced in improving data quality at the enterprise level, ranked by perceived importance Number of participants ranking the challenge in 1st or 2nd place, from a total of 43 respondents

Biggest challenges

Lack of front-ofÞce controls (e.g., poor quality of data entry at system 13 of origin with no/limited validation)

InefÞcient data architecture (e.g., multiple data warehouses with no 12 common data model, legacy systems, complex lineage)

Lack of business buy-in for value of data transformation 12

Data doesn’t get enough board and senior management attention 12 (e.g., seen as an IT issue, not considered a business asset)

Lack of central direction in driving transformation (e.g., disparate 10 BU-led efforts)

Ineffective governance model (e.g., unclear ownership of data, weak 9 or unenforced policies)

InsufÞcient funding/resource allocation for enterprise-level data 8 transformation program

Data transformation is driven primarily by regulatory compliance 6 needs; no focus on data quality

Manual effort required for reconciliation and remediation of 4 data-quality issues

Source: McKinsey analysis

53 McKinsey on Payments August 2018 Next it draws up a shortlist of use cases with ority use cases to generate quick wins, drive the greatest potential for impact, ensures they change, and provide input into the creation of are aligned with broader corporate strategy, a comprehensive business case to support the and assesses their feasibility in terms of com - overall data transformation. This business mercial, risk, operational efficiency, and finan - case includes the investments that will be cial control. These use cases can range from needed for data technologies, infrastructure, innovations such as new reporting services to and governance. more basic data opportunities, like the success - The final step is to mobilize data capabilities ful effort by one European bank to fix quality and implement the operating model and data issues with pricing data for customer cam - architecture to deploy the use cases through paigns, which boosted revenues by 5 percent. agile sprints, facilitate scaling up, and deliver Third, the institution prioritizes the use tangible business value at each step (Exhibit cases, taking into account the scale of impact 2). At one large European bank, this exercise they could achieve, the maturity of any tech - identified almost $1 billion in expected bot - nical solutions they rely on, the availability of tom-line impact. the data needed, and the organization’s capa - Actions: Select a range of use cases and priori - bilities. It then launches pilots of the top-pri - tize them in line with your goals; use top-prior -

Exhibit 2 Banks can deliver end-to-end use cases at speed via agile sprints.

Core principles Approach First quarter Second quarter

End-to-end delivery of End-to- Next product customer and internal end use to buy use cases case Churn analytics

Jointly owned by IT and the business Regulatory reporting

Financial reporting Vertically integrated agile teams extract, structure, … and surface data Foundations

Every short release delivers business value Data … … … … … architecture

Deliver minimum viable Data … … … … … products, making data governance and Þelds available only when needed

Source: McKinsey analysis

Designing a data transformation that delivers value right from the start 54

ity use cases to boost internal capabilities and warehouses, which will still be required to start laying solid data foundations. support tasks such as financial and regula - tory reporting. And data-visualization tools, 3. Design innovative data architecture to support the use cases data marts, and other analytic methods and Leading organizations radically remodel techniques will also be needed to support their data architecture to meet the needs of the business in extracting actionable in - different functions and users and enable the sights from data. Legacy and new technolo - business to pursue data-monetization op - gies will coexist side by side serving portunities. Many institutions are creating different purposes. data lakes: large, inexpensive repositories The benefits of new use-based data architec - that keep data in its raw and granular state ture include a 360-degree view of con - to enable fast and easy storage and access by sumers; faster and more efficient data multiple users, with no need for pre-pro - access; synchronous data exchange via APIs cessing or formatting. One bank with data with suppliers, retailers, and customers; and fragmented across more than 600 IT sys - dramatic cost savings as the price per unit of tems managed to consolidate more than half storage (down from $10 per gigabyte in 2000 of this data into a new data lake, capturing to just 3 cents by 2015) continues to fall. enormous gains in the speed and efficiency In addition, the vast range of services of - of data access and storage. Similarly, Gold - fered by the hundreds of cloud and specialist man Sachs has reportedly consolidated 13 providers—including IaaS (infrastructure as petabytes of data into a single data lake that a service), GPU (graphics-processing unit) will enable it to develop entirely new data- services for heavy-duty computation, and science capabilities. the extension of PaaS (platform as a service) Choosing an appropriate approach to data computing into data management and ana - ingestion is essential if institutions are to lytics—has inspired many organizations to avoid creating a “data swamp”: dumping raw delegate their infrastructure management to data into data lakes without appropriate third parties and use the resulting savings to ownership or a clear view of business needs, reinvest in higher-value initiatives. and then having to undertake costly data- Consider ANZ’s recently announced partner - cleaning processes. By contrast, successful ship with Data Republic to create secure banks build into their architecture a data- data-sharing environments to accelerate in - governance system with a data dictionary novation. The bank’s CDO, Emma Grey, noted and a full list of metadata. They ingest into that “Through the cloud-based platform we their lakes only the data needed for specific will now be able to access trusted experts and use cases, and clean it only if the business other partners to develop useful insights for case proves positive, thereby ensuring that our customers in hours rather than months.” investments are always linked to value cre - ation and deliver impact throughout the Actions: Define the technical support needed data transformation. for your roadmap of use cases; design a modu - lar, open data architecture that makes it easy However, data lakes are not a replacement to add new components later. for traditional technologies such as data

55 McKinsey on Payments August 2018 4. Set up robust data governance to tion that knows the data, possesses the levers ensure data quality to manage it, and is accountable for data qual - The common belief that problems with data ity, with metadata management (such as quality usually stem from technology issues is mapping data lineage) typically carried out by mistaken. When one bank diagnosed its data “data stewards.” A central unit, typically led quality, it found that only about 20 to 30 per - by a chief data officer, is responsible for set - cent of issues were attributable to systems ting up common data-management policies, faults. The rest stemmed from human error, processes, and tools across domains. It also such as creating multiple different versions of monitors data quality, ensures regulatory the same data. compliance (and in some cases data security), Robust data governance is essential in im - supports data remediation, and provides proving data quality. Some successful finan - services for the business in areas such as data cial institutions have adopted a federal-style reporting, access, and analytics. framework in which data is grouped into 40 Best-in-class institutions develop their own to 50 “data domains,” such as demographic tools to widen data access and support self- data or pricing data. The ownership of each service data sourcing, like the search tool one domain is assigned to a business unit or func - bank created to provide users with key infor -

Exhibit 3 A custom-designed search tool provides users with key information on data elements.

A DeÞnition of the term being Basic searched deÞnition

B Details of the data owner and Data history of owner ownership

C Navigation of the data tree to trace Data the search term’s lineage components

D Indicator of quality: red, Data amber, or green quality

E Good-quality source of the Golden data source

Source: McKinsey analysis

Designing a data transformation that delivers value right from the start 56 mation about the definition, owner, lineage, Actions: Assess data quality; establish robust quality, and golden source of any given piece data governance with clear accountability for of data (Exhibit 3). Organizations with read - data quality; provide self-service tools to facili - ily accessible information and reliable data tate data access across the whole organization. quality can deliver solutions much more 5. Mobilize the organization to deliver quickly and with greater precision. They can value also create enormous efficiencies along the Successful data transformations happen whole data lifecycle from sourcing and ex - when a company follows an approach driven traction to aggregation, reconciliation, and by use cases, promotes new ways of working, controls, yielding cost savings that can run as and mobilizes its whole organization from high as 30 to 40 percent. the beginning. Adopting a use-case-driven

Exhibit 4 Data governance is rolled out domain by domain.

Cluster data Plan the roll-out of data domains in waves Implement data governance into domains in each data domain Systems Data of records domains Roll-out plan by data domain Area Key activities

Year 1 Year 2 Year 3 Data domain Data deÞnition manage- Pilots 2nd wave 4th wave ment IdentiÞcation of common data Q1 Collateral Accounts Business elements Customer & data Product banking data Q2 Mapping of data Customer catalog Securities lineage data General Insurance Population of data tables Private dictionary Employees banking Choice of “golden Mortgage sources” Consumer DeÞnition of lending security Consumer requirements deposits

Collateral 1st wave 3rd wave 5th wave Assessment of data Data current data quality Q3 Risk Commercial Other data quality & management lending domains DeÞnition of KQIs Q4 MIS data Commercial Design of real estate Finance data data-quality Regulatory Leasing controls reporting Treasury Number of Number of management systems domains Operational DeÞnition of tools risk Capital Data tech- required for ~1,400 50–100 markets nology implementation and tools

Source: McKinsey analysis

57 McKinsey on Payments August 2018 approach means developing target data archi - enables 80 percent of use cases. Second, the tecture and data governance only when it is bank developed a rollout plan for implement - needed for a specific use case. One European ing data architecture and governance in three bank implemented this approach in three to four data domains per quarter. steps (Exhibit 4): Third, the bank set up a cross-functional First, it identified the data it needed for key team for each data domain, comprising data use cases and prioritized those data domains stewards, metadata experts, data-quality ex - that included it. Typically, 20 percent of data perts, data architects, data engineers, and

Exhibit 5 Banks need new roles to compete effectively in a data-driven market.

Senior Translator Data Data Head of Data Data executive scientist owner data quality technology governance manager manager Digital culture

Design and Design and Design and Fundamentals Fundamentals Fundamentals Fundamentals agile thinking agile thinking agile thinking of data of data of data of data management governance quality technology tools

Use-case Source of Source of Data culture Data Data Data reßections value value management management management

Best Best Data quality Data quality Data practices in practices in tools modeling data testing and engineering piloting

Best Technical Data design practices in leadership data program management

Best practices Data science Data in data software modeling ecosystem

Technical Advanced leadership analytics program

"Train the trainer“ approach

Source: McKinsey analysis

Designing a data transformation that delivers value right from the start 58 platform engineers. Before data was ingested to develop new capabilities; only 20 percent into the data lake, these teams worked to of the banks we surveyed believe they already identify key data elements, select golden have adequate capabilities in place. Given the sources, assess data quality, carry out data scarcity of external talent, in particular for cleansing, populate the data dictionary, and key roles such as business translators, organi - map data lineage. Each team worked in agile zations will need to provide on-the-job train - sprints in a startup-like environment for ing for employees involved in the three to four months. A central team took transformation, and complement this effort care of value assurance and defined common with a data and analytics academy to build standards, tools, and policies. deep expertise in specialist roles (Exhibit 5, page 58). This approach delivered numerous benefits for the bank, including rapid implementation, Actions: Adopt a use-case approach to the capability building, and the creation of tangi - whole journey; establish central governance to ble business value at every stage in the jour - ensure cross-functional working, the use of ney. During any transformation, calling out standard methods, and clear role definition; and celebrating such achievements is critical. build new data capabilities through hiring and As the CDO of JPMorgan Chase, Rob Casper, in-house training. observed, “The thing that achieves buy-in and * * * builds momentum better than anything is success . . . trying to deliver in small chunks In the past few years data has been estab - incrementally and giving people a taste of lished as a fundamental source of business that success [is] a very powerful motivator.” value. Every financial institution now com - petes in a world characterized by enormous More broadly, senior executives need to data sets, stringent regulation, and frequent champion their data transformation to en - business disruptions as innovative ecosys - courage widespread buy-in, as well as role- tems emerge to break down the barriers be - modeling the cultural and mindset changes tween and across industries. In this context, they wish to see. Formal governance and per - a data transformation is a means not only to formance-management systems, mecha - achieve short-term results, but also to nisms, and incentives will need to be embed data in the organization for long- rethought to support new ways of working. At term success. the same time, most organizations will need

Chiara Brocchi is an expert in McKinsey’s Milan office, where Davide Grande is a partner. Kayvaun Rowshankish is a partner and Allen Weinberg is a senior partner, both in the New York office. Tamim Saleh is a senior partner in the London office.

59 McKinsey on Payments August 2018 Building an effective analytics organization

As companies recognize the predictive power of advanced analytics, many are hoping to use AA to drive their business decisions and strategies. While most companies understand the importance of analytics and have adopted common best practices, fewer than 20 percent, according to a recent McKinsey survey, have maximized the potential and achieved AA at scale.

Gloria Macias-Lizaso In working with a wide range of organiza - pertise, beyond specific sector expertise, will tions, McKinsey has seen many companies become more and more relevant. start their analytics journey eagerly, but With this in mind, McKinsey conducted an without a clear strategy. As a result, their ef - extensive, primary research survey of over forts often end up as small pilots that fail to 1,000 organizations across industries and ge - scale or have significant impact. Some of ographies to understand how organizations these pilots have been mere exercises in “in - convert AA insights into impact, and how tellectual curiosity” rather than a serious ef - companies have been able to scale analytics fort to change the business. Consequently, across their enterprise (see sidebar on page they are not designed with an end-to-end ap - 61). In this article, we will discuss how to de - proach that incorporates the necessary con - sign, implement, and develop the right organ - ditions for implementation. Instead, the ization and talent for an AA transformation. pilots are carried out in small labs with lim - An AA transformation usually requires new ited connection to the business, and fail to skills, new roles, and new organizational provide the answers the business needs to structures. move forward. Even if a pilot does answer the right questions, it may not address the cul - Building an AA-driven organization tural aspects that would, for example, make a Top-performing organizations in AA are en - sales representative trust a model more than abled by deep functional expertise, strategic her own experience. partnerships, and a clear center of gravity for organizing analytics talent. These companies’ These companies quickly become frustrated organizations usually include an ecosystem of when they see their efforts falling short while partners that enables access to data and tech - more analytically driven companies are lever - nology and fosters the co-development of an - aging their data. Democratization of data is alytics capabilities, as well as the breadth and blurring sector boundaries; businesses will depth of talent required for a robust program increasingly find themselves disrupted not by of AA. the company they have been monitoring for the last several years, but by a newcomer For a company aspiring to an AA transforma - from another industry. Being the best in an tion, these elements can be incorporated into industry is no longer enough; now companies any of several organizational models, each of must aspire to be at least at par across indus - which is effective as long as there is clear gov - tries to compete effectively. Functional ex - ernance, and the company encourages an an -

Building an effective analytics organization 60 McKinsey’s Insights to Outcome Survey

In the fall of 2017, McKinsey performed quantitative research (using a survey-based approach) of approximately 1,000 organizations across industries and geographies. The survey contained 36 questions, most of which measured respondents’ degree of agreement or asked respondents to choose their top three responses. The 1,000 responses encompassed more than 60 responses per geography and over 50 responses per industry, which ensured statistical relevance in various cuts of the data. The responding companies represent more than $1 billion in revenues. The survey targeted analytics leaders and C-level executives with a broad perspective on their organization’s analyt - ics capabilities across the enterprise. These respondents included 530 individuals in analytics roles and 470 in busi - ness roles. The industries covered by the survey included: A&D, automotive, banking, insurance, energy (including oil and gas), resources (including mining and utilities), telecom, high tech, consumer, retail, healthcare, pharmaceuticals, trans - portation, and travel. The geographies covered included: US, UK, France, Germany, Spain, Brazil, India, Australia, New Zealand, Singapore, China, Japan, and the Nordics.

alytical culture across business units to learn work effectively, as long as governance is and develop together. Answering a few key established to prevent the various units questions can help to identify the best model. from becoming islands. The proposed or - ganization depends somewhat on how ad - 1. Centralized, decentralized, or a hybrid: vanced the company and the business units First, the company should decide whether are in their use of analytics. to create one centralized AA organization, in which AA stands alone in a center of ex - It is important to note that any organiza - cellence (COE) that supports the various tion will change over time as the AA trans - business units; a decentralized organiza - formation evolves. Some companies start tion, in which analytics is embedded in in - out decentralized and eventually move AA dividual businesses; or a hybrid, which into a centralized function, while others combines a centralized analytics unit with that are centralized later move into a hy - embedded analytics areas in some units. brid model of hubs and spokes. Top-per - forming companies prepare for these Our benchmark of several organizations eventual changes. indicates that any of these models can

One organizational example

A large financial and industrial conglomerate created a separate COE that reports directly to the CEO and supports the organization with AA expertise, AA resources (on “loan”), use case delivery, infrastructure to execute use cases, and technical interviewing. The center also manages data partnerships, develops new businesses by designing and deploying cross-company and ecosystem use cases on the company’s own infrastructure, facilitates aggregated AA impact calculation, reports progress to the executive committee, and executes the data committee’s mandates. The center started out as a small cost center but aspires to transform into a self-standing profit center within two years.

61 McKinsey on Payments August 2018 Two successful strategies

One industry conglomerate addressed this scale requirement by starting with a centralized COE serving all business units. As the use and understanding of analytics grew across the organization’s companies, they demanded more support, and the COE was split into sub-groups that were fully dedicated to the largest companies. Over time, own - ership of these groups was transferred to the “client” company—but not until they had built a sense of community and common methodology across the entire conglomerate. This sense of community was further reinforced by requir - ing all new recruits to spend six months at the COE and to go through specific AA training and networking events. Since fragmentation of the analytical talent across functions is almost inevitable over time, it is critical to start out with the appropriate processes and mechanisms to ensure consistency and community across these new profiles. A leading pharmaceutical company developed an integrated talent strategy that merged business and analytics func - tions. The company recruited technology and analytics executives in key management roles and developed analytics career paths for them. Placing analytics professionals in key business roles enabled the company to identify and op - erationalize new analytics opportunities before their competitors could. The organization successfully embedded ana - lytics in key elements of the business—for example, analytics on clinical trial data to enable more cost-effective data.

The choice between centralization and de - the best approach? AA will effectively be - centralization is not an all-or-nothing de - come the “brain” of the organization, so cision but should be decided per companies should be careful not to out - sub-function. Data governance, however, source too much. Top-performing compa - should be centralized, even if data owner - nies often keep analytics that provide a ship is not. For data architecture, top-per - competitive advantage—such as pricing an - forming companies often have data alytics—within the organization. A central, centralized within business units. This internal unit can oversee all AA outsourc - data typically includes data from market - ing, and partnerships can be established ing, sales, operations, and so on. Most top- for specific AA solutions or to bring in par - performing companies centralize ticular assets, such as unique sources of partnership management; otherwise, com - data or advanced solutions. peting or redundant partnerships could in - 3. Locating the AA unit: Yet another impor - advertently be set up in various parts of the tant decision is where to locate the AA unit. organization, and intellectual property AA is most effective when it is cross-func - could be at risk. tional, accessible enterprise-wide, and inte - 2. To outsource or not to outsource: An - grated with the business. Various levels and other decision is whether AA talent should functions can host it, but the final location be partially outsourced, and if so, how. should have enough visibility and access to Should outsourcing be limited to low-level the C-suite to break through inertia and en - data analytics activities? Or should the able transformation. It is helpful if the unit company establish several tactical partner - has an enterprise-wide view, given its trans - ships for selected tasks? Or would a strate - formational potential for all functions. gic partnership with an external vendor be

Building an effective analytics organization 62 The AA unit is often most effective when it orities. Including AA within marketing or is a sub-unit of business intelligence—as operations, meanwhile, can limit its poten - long as this area has an enterprise-wide tial to transform the remaining parts of the perspective—or of strategy or digital. Some organization. companies locate their AA units in IT, but Staffing the AA center of excellence this arrangement can be challenging. IT Sixty percent of top-performing companies staff—who are used to managing longer- in AA have a “center of gravity” for their an - term projects that are often disconnected alytics efforts, according to our survey. They from the business—may not be prepared to typically include a specific set of roles, skills, manage short-term, agile AA projects. AA and capabilities within the COE (Exhibit 1), projects can end up last on their list of pri -

Exhibit 1 An organizational blueprint of the advanced analytics COE.

Advanced Analytics COE Head of Advanced Analytics COE

COE support ofﬁce (recruiting, talent management, etc.)

Demand Analytics Analytics Analytics Data Training management development support platform governance

HR Program Deployment DWH/DM Metadata managers managers specialist design

Oil and gas Data Operations Data Data scientists support architects compliance manager

...Solution Data Data Data architects quality operations governance managers manager

Business Help desk analysts

Developers

Data acquisition managers

Source: McKinsey analysis

63 McKinsey on Payments August 2018 Lessons learned from COE failures

Companies that have rolled out full-scale COEs during an AA transformation have encountered some pitfalls. Some of the most common include: 1. A failure to focus on business value. Successful roll-out of a COE requires a clear vision as to where the COE’s biggest business impact can be captured. The COE should be linked to the organization’s overall business strategy and should commit to achieving a measurable impact during a defined time frame, with its goals clearly prioritized.

2. Over- and under-thinking technology. Some companies attempt to replicate an entire data history when building their AA COE, resulting in data “scope creep.” It is vital to sufficiently think through data integrity/architecture; failing to do so may result in missing data and missing data connections. Some companies try to do too much at once by replacing their hardware, software, and analytics stack simultaneously rather than tackling one at a time. These com - panies may buy the “best of breed” in each category but then find that none of them “talks” to each other. Instead, companies should build systems and functionality as needed—especially since technologies tend to become obso - lete within just a couple of years. New innovations can be integrated later if the system is built gradually.

3. Taking more than 18 months to deliver value. The COE’s benefits should begin to come online well before the en - tire roll-out is complete. If the COE does not deliver benefits sooner, it is often because it depends too heavily on in - sights-delivery FTEs instead of automation. The delivery of insights should be staged to capture value sooner.

4. Insufficient skill-building and change management. Top management and the internal team must be 100 per - cent committed to the COE if it is to succeed. Internal stakeholders must be engaged in development or accountable for delivery. The effort cannot focus exclusively on technology instead of the process and people; in particular, the or - ganization must build the requisite front-line skills needed for an effective AA COE. Companies should hew closely to the business case and avoid the functional scope creep that can occur when mid-flight changes not included in the business case suddenly become priorities.

5. Operating analytics as an island. One large US insurance company interviewed by McKinsey hired a sizeable number of data scientists and launched more than 50 pilot projects to test its new capabilities. Despite a real com - mitment and considerable investment, the analytics team was isolated from the rest of the company, with no connec - tion to the overall business strategy—a critical mistake. Not surprisingly, this company’s ad hoc analytics projects had no real impact.

At the other end of the spectrum, successful AA-driven companies are building centralized AA capabilities and then creating end-to-end agile teams (“use case factories”) that integrate profiles from IT, sales, marketing, finance, and other functions. This approach ensures that use cases are immediately integrated into business processes and thus create value.

including data scientists (“quants”), data en - tween the COE and business units. The gineers, workflow integrators, data archi - translators usually have a combination of tects, delivery managers, visualization business, analytics, and technology skills analysts, and, most critically, translators and are found in the business partner role in from the business who act as a bridge be - data analytics leadership.

Building an effective analytics organization 64 Many COE roles are filled with highly special - mine how those models will be put into pro - ized analytical resources recruited from ad - duction; and learn from the results. vanced degree programs in computer science The COE can be built in about 18 months, typi - or math. But these individuals must also be cally in incremental steps. It may start with five able to translate sophisticated models into to ten data professionals, including data engi - simple, visual decision support tools for neers, data scientists, and translators. In its front-line employees. end-state, it likely will require significantly They also need to have a collaborative mind - more. The number of translators needed will set, given the interdependencies among data, vary by business unit but is generally about 10 systems, and models. With translators bridg - percent of business unit staff. Most companies ing any communication gaps, team members source their translators from “client” business from analytics and the business work to - units and then train them, since these employ - gether in two- to three-month agile “sprints” ees will have deep knowledge of the processes as they identify problems; find out whether that AA is trying to optimize. These individuals relevant data exists and, if not, whether that are usually analytical, critical thinkers who are data can be acquired; test their models; deter - well respected in the company.

Exhibit 2 Beyond the COE, employees at all levels need to be trained.

Senior leaders Managers Business leaders of a Business users (i.e. mid-level transformation or a use case managers) of the outputs of analytics implementation models and insights

Board

Top team

Analytics COE BU 1 BU 2

Data Data Design science engineering

Analytics specialists Analytics translators Design and build analytics models Collaborate with the business to Iterate/reﬁne models based on understand the need, and can guide business and user feedback analytics design to meet this need

Source: McKinsey analysis

65 McKinsey on Payments August 2018 To illustrate how the various key skills and roles come together in the COE, here is an example description of these roles’ working together to fulfill a business request: • The translator and business owner identify and prioritize the business request.

• The data scientist works with the translator to develop an analytics use case, including an algorithm and analyses to tes t.

• A data engineer from the COE works with the relevant business division to understand the data requirements of the use case and to identify data sources.

• The data engineer works with IT/the business to ensure data availability, identify gaps, and develop ETL (extract, trans - form, load) to load data into analytics sandbox.

• A data scientist programs the algorithm and analyzes the data in the sandbox to generate insights.

• A visualization analyst develops reports and dashboards for business users.

• A COE workflow integrator works with the business owner to develop a prototype for models and tools.

• The COE ensures that key business and IT stakeholders test the prototype tools and solutions.

• A delivery manager pilots the prototype and dashboard and works to obtain a go/no-go decision.

• The delivery manager and COE workflow integrator work with IT to scale the prototype to the enterprise level.

• The COE delivery team and translator work with the business and IT to ensure adoption and ongoing model maintenance.

In this process, feedback would be gathered between steps nine and ten.

While the COE and some of its roles may In McKinsey’s survey, 58 percent of respon - emerge gradually, it is best to have the data, dents at top-performing companies say that platform, and career paths needed for an AA their organization has deep functional ex - transformation in place from the beginning. pertise across data science, data engineering, If the platform is still under development, data architecture, and analytics transforma - adding more people may only make that de - tion. Top-performing organizations have four velopment more complicated. And without a times as many analytics professionals and clear career path, attracting this scarce talent one and a half times more functional experts will be difficult. As much as possible, roles than other companies. should be clearly delineated to prevent These companies also retain three times squandering valuable talent on functions for more talent—primarily by creating strong ca - which they are over-qualified, which can un - reer development opportunities. People with dermine retention. superior analytics talent usually have many Career development and strategic potential opportunities and thus need to see a partnerships clear career path and opportunities for Gaining an edge in analytics requires attract - growth within a company if they are to join or ing, retaining, and sourcing the right talent. stay with it. Several career tracks should be

Building an effective analytics organization 66 available, as some analytics staff may wish to Beyond the COE: training employees for pursue a more technical profile, others may cultural change move into translator or integrator roles with As detailed in “Hidden figures: The quiet the business, and some will likely move into discipline of managing people using data,” managerial positions. on page 36, an AA transformation requires a profound cultural change, as the entire or - In all cases, these individuals tend to stay moti - ganization must change the way it operates. vated if they are learning on the job and from Employees need to learn to trust in AA, to one another. Achieving this goal requires a understand what they can ask of it, and to minimum scale for each analytics group. Hav - know that AA can answer far more complex ing only one or two data scientists in each func - questions than traditional analytics ever tion will not help them learn, and they may could. Outside of the COE, then, employees have difficulty making themselves understood. at all levels—senior leaders, managers, ana - To fill any gaps in talent, 62 percent of survey lytics specialists, and analytics translators— respondents at top-performing companies need to be trained to be AA-proficient and to say that they strategically partner with others drive the transformation forward (Exhibit 2, to gain access to skill, capacity, and innova - page 65). tion. For example, a large, multinational re - A sweeping—but feasible—transformation tailer developed a strategic partnership with Transforming a company to be AA-driven is a a start-up incubator that focuses on identify - monumental task that should not be under - ing cutting-edge technologies—such as taken in one fell swoop, but instead incre - drones—to transform the retail industry. The mentally, based on use cases. Since AA can retailer found that employing a mix of in- and will transform a company, the effort to house talent and smart, strategic partner - cultivate an AA-driven organization is most ships with other organizations enabled it to effective when it comes from the top, from get the best out of both, thus affording access senior executives. If a company focuses on to skills, capacity, and innovation on a much the value of advanced analytics and builds AA larger scale. Through the incubator, the re - capabilities as needed—while still having the tailer formed partnerships with start-ups and data, platform, and talent strategy in place venture capital investors. The company also from the beginning—its AA transformation created a compelling value proposition for at - will succeed. tracting top analytics talent.

Gloria Macias-Lizaso Miranda is a partner in McKinsey’s Madrid office.

67 McKinsey on Payments August 2018 “All in the mind”: Harnessing psychology and analytics to counter bias and reduce risk

The management of risk in financial services is about to be transformed. A recent McKinsey paper identified six structural trends that will reshape the function in the next decade. Five are familiar—they concern regulation, costs, customer expectations, analytics, and digitization—but one is less so: debiasing. That means using insights from psychology and behavioral economics, combined with advanced analytical methods, to take the bias out of risk decisions. The institutions pioneering this approach have seen tremendous benefits: for instance, banks adopting psychological interventions in consumer collections have achieved a 20 to 30 percent increase in the amount collected. 1

Tobias Baer The interest in debiasing is growing as psy - control and a lack of conscientiousness, traits Vijay D'Silva chological research uncovers more and more that are likely to determine which types of subconscious effects that influence our deci - decision bias this customer can be expected sion making. Meanwhile, an explosion in data to manifest. availability is providing businesses with an Compare this profile with that of a card - abundant flow of information for their ana - holder who completes one big supermarket lytic engines. Not all the data theoretically transaction at more or less the same time available can be exploited, for legal and pri - every Friday evening, with little or no evi - vacy as well as technical reasons. But institu - dence of convenience-store shopping in be - tions still have a massive amount of tween. That profile is indicative of a underused data that they can mine, using an well-organized person who plans ahead. It’s increasingly sophisticated array of advanced likely that the first customer would benefit analytics techniques, to develop behavioral from financial products designed to help cus - segmentations and predictive models. With tomers who struggle to meet their financial these foundations in place, they can go on to obligations— such as a credit card with design powerful interventions to tackle bias. weekly rather than monthly payment install - Take the example of a bank using a recursive ments—whereas the second customer would neural network to extract customer profiles probably have no need of them. And if, say, from credit-card transaction data. One pro - the bank is considering ways to motivate file that emerges is of a cardholder who clocks cardholders to pay off delinquent credit, its up dozens of low-value transactions at a con - knowledge that customers with the first card - venience store every week. The customer’s holder’s profile are likely to prioritize imme - habit of making multiple repeat visits at odd diate consumption over clearing their debts 1 For a comprehensive discussion of the psychological levers that can be used to hours—seemingly for only one or two items at will help it design suitable incentives to improve performance in consumer debt collection, see Tobias Baer, a time—suggests a lack of forward planning. counter this tendency. “Behavioral insights and innovative Seen through a psychometric lens, the cus - treatments in collections,” McKinsey Analytics-driven psychological insights like on Risk , Number 5, March 2018. tomer seems to be exhibiting poor impulse these can be a spur to tremendous value cre -

“All in the mind”: Harnessing psychology and analytics to counter bias and reduce risk 68 ation. This article considers some of the most common biases in business decision making A quick guide to common biases and looks in detail at three areas where debi - Heuristic biases are computational shortcuts asing can reap rich rewards: credit under - taken by the brain to achieve lightning-fast, al - writing, consumer debt collection, and asset most effortless decision making. Thanks to the management. Nobel Prize–winning work of Daniel Kahneman Uncovering biases in business and Richard Thaler, these biases have become Biases are predispositions of a psychological, more widely understood in recent years. More sociological, or even physiological nature that than a hundred have been identified, ranging from can influence our decision making (see side - the relatively familiar loss aversion to the less well- bar, “A quick guide to common biases”). They known Hawthorne effect. For practical business often operate subconsciously, outside the log - purposes, five groups of biases are key: ical processes that we like to believe govern • Action-oriented biases prompt us to act with our decisions. They are frequently regarded less forethought than is logically necessary or as flaws, but this is both wrong and unfortu - prudent. They include excessive optimism nate. It’s wrong because biases are an in - about outcomes and the tendency to underes - evitable side-effect of the mechanics our timate the likelihood of negative results; over - brains need to achieve their astonishing confidence in our own or our group’s ability to speed and efficiency in making tens of thou - affect the future; and competitor neglect, the sands of decisions a day. And it’s unfortunate tendency to disregard or underestimate the re - because the negative perception of biases sponse of competitors. leads us to believe we are immune to them—a • Interest biases arise where incentives within bias in itself, known as overconfidence, ex - an organization or project come into conflict. hibited by the 93 percent of US drivers who They include misaligned individual incentives, believe themselves to be among the nation’s unwarranted emotional attachments to ele - top 50 percent. ments of the business (such as legacy prod - Even if we accept that biases may influence ucts), and differing perceptions of corporate our decisions, we might assume that success - goals, such as how much weight to assign to ful organizations have developed processes to particular objectives. keep them in check. But experience indicates • Pattern-recognition biases cause us to see otherwise. For example, academic research nonexistent patterns in information. They in - has found that ego depletion materially af - clude confirmation bias , in which we overvalue fects the work of judges, doctors, and crime evidence that supports a favored belief and investigators, and our own research has re - discount evidence to the contrary; availability vealed how it affects credit officers’ decisions, bias, in which we misperceive likelihoods of manifesting itself in tangible business met - events because we recall one type of event rics such as credit approval rates. When fi - much more easily (and hence frequently) than nancial institutions work to counter bias in others; management by example, in which we judgmental underwriting—in small business rely unduly on our own experiences when mak - credit, for example—they can typically cut

69 McKinsey on Payments August 2018 ing decisions; and false analogies , faulty thinking based on and sunflower management , the tendency for group mem - incorrect perceptions and the treatment of dissimilar things bers to fall into line with their leaders’ views. as similar. For all their importance, however, heuristic biases represent • Stability biases predispose us toward inertia in an uncer - only the tip of the iceberg as far as subconscious influences tain environment. They include anchoring without sufficient on our decisions are concerned. Exhibit A illustrates other adjustment , in which we tie actions to an initial value but fail factors that lie deep below the surface. Somatic and emo - to adjust when new information becomes available; loss tional effects tinker with the parameterization of our brain, aversion, the fear that makes us more risk-averse than logic and can be triggered by factors as diverse as blood-sugar would dictate; the sunk-cost fallacy , where our future course level, smells, or mood: for instance, if our blood sugar is low, of action is influenced by the unrecoverable costs of the we (quite reasonably) estimate that completing a given task, past; and status-quo bias , the preference for keeping things such as climbing a mountain, will take us longer. Ego deple - as they are when there is no immediate pressure to change. tion, a form of mental fatigue, leads us to move from logical thinking to unconscious short-cuts that favor easy default • Social biases arise from our preference for harmony over decisions. And group psychological effects override rational conflict, or even constructive challenge. They include decision making out of a deep-seated fear of ostracism. groupthink , in which the desire for consensus prevents us making a realistic appraisal of alternative courses of action,

Exhibit A

Psychologists and Logical reasoning neuroscientists have discovered many forces Heuristic mechanisms take shortcuts 1 that are prone to biases that cause decisions to gravitate away from 2 Somatic and emotional effects distort logical considerations. evaluative thinking Ego depletion reduces capacity for 3 self-control

Group psychological effects can 4 override logical assessments

Gap to logical Source: McKinsey analysis answer

“All in the mind”: Harnessing psychology and analytics to counter bias and reduce risk 70 credit losses by at least 25 percent, and even course of the working day as ego depletion as much as 57 percent in one case. sets in (Exhibit 1). The good news is that com - panies aware of this phenomenon can make For lenders, an area particularly ripe for debi - adjustments in collectors’ working environ - asing is debt collections, where biases can ment to help counter it. shape the behavior of collectors and cus - tomers alike. Consider how collectors handle And when it comes to customers with over - calls with recalcitrant customers. Over the due accounts, leading financial institutions course of a call, they need to make numerous are harnessing a plethora of psychological in - split-second decisions that expose them to sights to encourage payment. This often the full gamut of biases, such as anchoring means making targeted interventions that in - and over-optimism, as well as somatic effects crease customers’ motivation to pay, help and ego depletion. Whether they persist in those with low self-control to keep their com - trying to elicit a promise to pay or give up and mitments, and respect individuals’ need for move on to the next delinquent account may agency (and thereby avoid triggering what partly depend on the time of day. The effec - psychologists call “reactance”). A credit-card tiveness of collectors’ calls dwindles over the provider could, for instance, present high-

Exhibit 1 Call effectiveness dwindles over the course of the day through ego depletion as collectors tire.

Case example; percent of calls eliciting promise to pay, by time of day

0 9.0010.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00

Source: McKinsey analysis

71 McKinsey on Payments August 2018 How can financial institutions tackle biases?

The questions organizations need to consider include: The decision type: High or low frequency, formal or informal Formal high-frequency decisions, such as credit underwriting or standard manual fraud checks, lend themselves to analytical solutions coupled with “industrial strength” psychological interventions. For example, a bank that usually asks about the fre - quency of CFO changes in the past three years—a question that may be susceptible to the availability bias—could instead design a simple table prompting credit officers to construct a timeline for pertinent data points. Formal low-frequency decisions—such as approvals of new lending products or a credit committee’s quarterly recalibration of the PD rating model that drives underwrit - ing and risk-based pricing—call for decision processes to be redesigned to support logical thinking and ensure adequate challenge. Analytical modeling is often helpful here. One US bank used four different econometric models to produce four distinct default-rate forecasts in an elegant effort to counteract groupthink and introduce au - tomated “devil’s advocates” into the discussion. Informal decisions, such as a supervisor’s override on a policy violation, may first have to be formalized before any intervention can be deployed. A review of historical losses may shed light on a few decision types that warrant such an investment, such as debt collectors’ decisions to give up on difficult accounts. If a bank wants a collector to spend longer than usual on a call to a particular customer, for instance, it could flag up an above-average incentive payment in a pop-up on the collector’s screen.

Who to target and how Institutions need to use behavioral segmentation to distinguish which groups are af - fected by which primary biases, and which personality traits determine the choice of countermeasure. In consumer debt collection, for instance, the psychological need for agency can cause customers to resist resolution if they feel they have been put on the spot by a call from an assertive collector. An invitation to restructure the debt on a self-service website could effectively overcome this bias. However, this same ap - proach could be disastrous if used to deal with a customer who is biased toward avoidance.

The role of automation Carefully designed algorithms can not only speed up decisions and take out costs, but also remove biases from a growing range of decision types. But financial institu - tions must beware of a major trap: building past biases into the algorithm.

“All in the mind”: Harnessing psychology and analytics to counter bias and reduce risk 72 risk customers with a late-fee waiver or a gift hibit 2). By way of comparison, comprehen - card from a favorite shop that they would lose sive best-practice models for rating small if they didn’t make a payment. Framing the businesses can achieve a Gini of 60–75. offer as a loss for a payment missed, rather In fact, half of the dimensions in the bank’s than a reward for a payment made, enlists the rating model achieved a Gini score of 7 or help of the loss aversion bias and can double lower—little better than a roll of the dice—yet the effectiveness of the offer. the bank was paying them just as much atten - Before deciding where and how to use behav - tion as it gave to dimensions with genuine ioral levers, financial institutions need to con - predictive power. For instance, despite scor - sider a range of factors (see sidebar, “How can ing a Gini of just 1 in back-testing, share - financial institutions tackle biases?”, page 72). holder composition was usually discussed in depth in credit memos, and relationship man - To give a sense of what can be achieved when agers were even prompted to ask customers these techniques are applied in practice, let’s follow-up questions about it. Factoring in now examine what leading institutions have such irrelevant dimensions anchored credit been doing to take bias out of credit under - officers’ overall rating in randomness, drag - writing and consumer debt collection. And ging it down to a Gini of just 22. looking beyond lending, the sidebar “Debias - ing asset management” (page 76) describes In order to debias its commercial underwrit - how firms in an adjacent industry uncovered ing, the bank had to separate the wheat from bias in their investment decisions. the chaff—a systematic process combining analytics with psychological insights. First, Commercial credit underwriting the bank replaced fuzzy concepts with care - Most credit officers possess a strong profes - fully chosen sets of proxies for which more sional ethic and have honed their skills over objective assessments could be developed. years, if not decades. Yet evidence indicates Eliminating factors that were irrelevant, or they are just as susceptible as anyone else to impossible to assess without crippling bias, decision bias. would substantially improve the overall One bank with poor performance in its com - credit rating. Second, explicit psychological mercial credit underwriting made a retro - “guard rails,” such as the use of tables to spective assessment of the predictive value of prompt credit officers to plot data along a its judgmental credit ratings using Gini coef - timeline rather than relying on a customer’s ficient measures on a scale from 0 (no predic - spontaneous recall of events, were put in tive power) to 100 (perfect prediction). The place to safeguard qualitative assessment analysis examined 20 dimensions stipulated processes from biases. by the bank’s credit policy, such as manage - Finally, the bank used statistical techniques ment quality and account conduct, and com - to validate each redesigned factor and cali - pared judgments made by credit officers with brate its weight. As is common in commercial actual defaults observed over the following 12 credit portfolios, the bank ran up against the months. One dimension (account conduct) problem of a small sample size. This was com - stood out with a relatively high Gini of 45, but pounded by the need to compile additional most dimensions had much lower scores (Ex -

73 McKinsey on Payments August 2018 data manually for the sample used in devel - Consumer collections oping the new assessment, which comprised A recent McKinsey survey of 420 US con - just 30 to 50 defaulters and the same number sumers with credit delinquencies sheds light of performing debtors. Although such con - on some of the decision biases that contribute straints ruled out the statistical techniques to non-payment. For instance, many con - most commonly used in credit scoring, such sumers are unable to resist the temptation of as logistic regression, the bank was able to de - immediate consumption—an example of ploy powerful statistical concepts from social what’s known as “hyperbolic discounting”— science instead, such as Cohen’s d and t-test. and so they struggle to manage money through a monthly cycle. A third of those sur - The bank has now been using its qualitative veyed expressed a preference for a schedule credit rating, with minimal annual adjust - that would allow payment every week or every ments, for more than a decade, scoring an other week, either because it would fit better overall Gini between 60 and 80 every year, with their paydays or because smaller, more even during the financial crisis.

Exhibit 2 One bank’s credit-rating model contained factors that were subject to bias or had little or no predictive power.

Case example; predictive power of judgmental ratings assigned by credit ofﬁcers, measured by Gini coefﬁcient: 0 = useless (random), 100 = perfect prediction

Predictive power of overall rating when all factors Predictive power of 20 individual rating components are combined (e.g., company’s management quality, account conduct, customer base)

Either debias the assessment of a speciÞc factor to achieve maximum predictive power… 45

…or eliminate the factor 30 if it distorts the overall 24 22 assessment 16 15 13 11 99 9 7 66 6 5 4 3 22 1 1234567 89 10 11 12 13 14 15 16 17 18 19 20

To debias credit memos, institutions can replace lengthy prose with concise questions, multiple-choice options, and simple tables—which will also streamline assessment, cut costs, and speed up turnaround

Source: McKinsey analysis

“All in the mind”: Harnessing psychology and analytics to counter bias and reduce risk 74 frequent payments would be less painful and smallest balance first (Exhibit 3). Banks that easier to manage than monthly bills. are aware of such motivations can either rein - force them with tailored payment plans or Understanding how consumers decide what help customers adjust their rationales—for to pay and when is particularly important instance, by breaking down large balances when they owe money to more than one into smaller chunks or milestones. lender. Only a third of survey respondents prioritized payments rationally by, say, tack - Some leading banks are putting behavioral ling debts with the highest interest rate first, targeting into practice by applying psycho - or seeking to retain their most useful credit metrics: the factual scoring of a customer’s card. The remaining two-thirds followed less personality profile according to a framework rational patterns: some apportioned pay - such as the widely used OCEAN Big Five. ments equally, others showed loyalty to a par - Such a profile allows banks to micro-target ticular bank, and yet others paid off the marketing messages not only in origination—

Exhibit 3 Research into consumers with credit delinquencies yielded valuable behavioral insights.

Survey of 420 US consumers who have been at least one month overdue

What payment frequency do you prefer? Comments and insights % Weekly “I always prefer to pay smaller 16 payments more frequently because it takes the sting out of making a payment. Making a Every two 18 large payment always feels like weeks a punch.” Monthly or 66 less often

When several accounts are overdue, which do you pay ﬁrst?1 Comments and insights % Longest-held card 20% of respondents said they 6 Each equally have withheld a planned Main bank 22 payment because of an 16 upsetting call from a collector

Smallest balance 9 38% of respondents had a very positive experience with

9 30 at least one collector who was Largest balance 6 Card with highest empathetic and genuinely interest rate helpful Card with most beneÞts

1 Figures do not sum to 100% because of rounding Source: McKinsey survey

75 McKinsey on Payments August 2018 Debiasing asset management

Few industries have subjected their investment decision-making processes to more scrutiny than asset manage - ment, yet biases still affect many high-value decisions throughout the lifecycle of individual funds. In the early stages of structuring a new fund’s strategy and processes, for instance, stability biases can influence whether an index or some other means is chosen for assessing performance. Interest biases, such as misaligned incentives, need to be monitored to ensure that the long-term interests of unit holders and asset owners are taken into ac - count when funds are managed and promoted. Leading asset management organizations are becoming increasingly alert to the impact of decision-making biases on fund performance too. A few have adopted an innovative approach to diagnosing bias and its drivers. Working with analytics experts and behavioral scientists, they have applied machine-learning algorithms to their own histori - cal data and discovered clusters of suboptimal investment decisions. Having examined these decisions more closely, they have detected signs of consistent bias in the processes by which the decisions were reached. When one such organization analyzed its trades, processes, and associated emotions for signs of bias, it found that more than 35 percent of fund managers’ decisions were influenced by biases such as loss aversion, anchoring, and what’s known as the “endowment effect,” in which we attach more value to items that we own. Dan Ariely, a behavioral economist and the best-selling author of Predictably Irrational, notes that this effect kicks in when indi - viduals fall in love with what they already have and focus on what they may lose rather than what they may gain. Such a sentiment can drive fund managers to hold on to stocks for too long and ignore better investment opportu - nities elsewhere—a trap into which many seasoned investors have fallen. In one of the funds that this organization examined, the endowment effect had led one fund manager to hold on to 20 percent of positions for too long. The stocks affected had underperformed the relevant index by an average of 25 percent in the 12 months prior to exit. The fund manager acknowledged that he had paid insufficient attention to these stocks, had not rated them as performing badly enough in absolute terms to divest, and could have tried harder to identify better investment opportunities. He admitted that if he had asked himself from time to time whether he would still buy the stocks today, he would have been unlikely to hold on to them for so long. In this case, the value left on the table as a result of the endowment effect was equivalent to 250 to 300 basis points per year. And this fund manager is not alone. According to Cabot research, institutional investors lose an average of 100 basis points in performance a year as a result of the endowment effect—or 250 basis points in the case of the 10 percent of most-affected funds. A typical debiasing process is a learning exercise for an asset management fund. By exposing patterns of bias with the help of analytics and then selecting and applying debiasing methods in its investment decisions, the fund will be able to target the specific biases and situations that affect its own investment decisions. From the many inter - ventions available to address every type of bias, it will need to select and customize measures that suit its fund mandate, investment philosophy, team process, culture, and individual personalities. 1

Magdalena Smith is an expert in McKinsey’s London office.

1 For more on this topic, see Nick Hoffman, Martin Huber and Magdalena Smith, “An analytics approach to debiasing asset-management decisions,” McKinsey & Company, December 2017.

“All in the mind”: Harnessing psychology and analytics to counter bias and reduce risk 76 choosing the visuals, tag line, and highlighted ative affect—will impede resolution. One ap - features to use in a product pitch—but also in proach that institutions have found effective debt collection. When applying such an ap - is to use collectors with profiles similar to proach, banks often find it helpful to break those of customers, matching regional di - down a collections episode into four distinct alect, gender, and age. Similarly, requesting a “moments”: call back via email, text message, or app alert instead of calling the customer directly shows 1. Opening . When the phone rings, customers respect for an individual’s need for agency. must decide whether or not to engage with Customers too ashamed or anxious to speak the bank or card provider. If they pick up, on the phone can sometimes be steered to they then have to decide whether to take a self-service channels through advertisements defensive or evasive stance or to collabo - on social media. rate in problem solving (for example, by disclosing financial difficulties). By telling a customer that the solution being offered has been popular with other clients, 2. Commitment . Once collaboration has been collectors can trigger the “herd effect”— one established, the collector needs to move of several techniques proven to move a cus - the customer toward a promise to pay. tomer towards a commitment. Anchoring ne - 3. Negotiation . A major part of the conversa - gotiations in a full repayment within a short tion will be a negotiation over the cus - time-frame will help a customer commit to tomer’s financial limitations and the making the biggest payment they can man - payment to which he or she is willing to age. This not only maximizes recovery for the commit. bank but also protects the customer from un - 4. Follow-through . Finally, the customer necessary interest charges and bankruptcy needs to keep the promise to pay—a com - that could result from falling victim to hyper - plex decision with ample opportunities for bolic discounting. derailment. Ensuring that customers keep their promise At each of these moments, the customer to pay is arguably the hardest part of collec - must decide whether or not to cooperate tions. Again, behavioral segmentation sheds with the lender, and the lender must try to light on the intricate factors determining the understand the customer’s behavior and decision to follow through—or not—on a identify opportunities to increase the likeli - promised payment. One justification cus - hood of repayment, using psychological in - tomers frequently use to rationalize broken terventions carefully calibrated to each promises is the hassle (actual or perceived) customer’s profile. involved in making a payment. A quarter of respondents in our survey of US consumers In the opening moment, a collector who puts with credit delinquencies said that making a customer in the right mood (or “positive af - payments was a hassle, and a third of this fect”) will increase that person’s receptive - group said they would be more likely to pay if ness to exploring solutions and more convenient payment methods were self-confidence in resolving the situation. available (Exhibit 4). Conversely, creating the opposite mood—neg -

77 McKinsey on Payments August 2018 Exhibit 4 For some consumers, payment can be a hassle.

How difﬁcult is it to execute a payment? With more convenient payment methods, % would you be more likely to make payments? Huge hassle % DeÞnitely not Probably not 5 8 4 No hassle Quite a hassle For sure 29 21 33

55 45 Probably yes Not much of a hassle

What payment method is easiest for you? Comments % “An option to use a prepaid card or Online payment 68 something like that would help. Nine times out of ten if the money gets Smartphone app 16 put in the bank account, it will be taken out by another bill.” Direct debit 10

Debit card 5 “I wish there was an easier way to

send payments from my debit PayPal 4 account. I hate Þnding out all the account numbers.” Physical location 6

Source: McKinsey survey

One bank that piloted innovative treatments real-life experience with nudges and other saw multiple benefits: a 30 percent increase psychological interventions in other indus - in collections, a 20 percent reduction in tries to design effective treatments.. write-offs on delinquent debt, a 33 percent * * * fall in delinquencies remaining for late-stage collection, and a 20 percent reduction in the Being aware of bias and taking deliberate number of customers subsequently relapsing steps to counter it has already proved effec - into default (Exhibit 5, page 79). First, the tive in areas such as gender bias in hiring. A bank used K-means clustering to create an few pioneering financial institutions have initial segmentation of five behavioral clus - adopted a similar approach to debiasing their ters. Next, it used a range of tools including business decisions and have seen impressive closed-file reviews, psychometric surveys, results, such as a 25 to 35 percent reduction and interviews to compile an ethnographic in credit losses from improved underwriting profile for each cluster. Finally, it drew on the and collections. Yet for most institutions, the growing body of psychological research and big prizes have yet to be captured.

“All in the mind”: Harnessing psychology and analytics to counter bias and reduce risk 78 Exhibit 5 Tailored treatments based on behavioral segmentation can deliver multiple beneﬁts.

Percentage of outstanding Impact measured 3 months later 1 balances to be for one segment collected %

100

Beneﬁts of behavioral pilot Collected 47 30% rise in total collected 61 20% fewer write-offs 33% fewer delinquencies Written off 5 remaining in back end for late-stage collection Still in back end 18 4 20% fewer relapses into default among collected 12 accounts Arrears collected but customer 29 defaulted again 23

Control Pilot group group

Behavioral-based prescriptive treatment for this segment High call priority; no delay in efforts because of messages being left Thorough inquiry with detailed questions about the customer’s situation Assertive script with no inappropriate “customer service” mindset Questions about how, where, and when customer will pay help form an “implementation intention” that makes them more likely to keep their promise

1 Figures may not sum to 100% because of rounding Source: McKinsey analysis

The secret lies in combining psychological in - implementation, providers can have a new sights with advanced statistical methods to approach up and running in as little as three develop a pragmatic but powerful behavioral months, with dramatic effects. Given the im - segmentation linked to targeted treatments. pact that early efforts have achieved, it can be By introducing creative workarounds into only a matter of time before such innovative their existing infrastructure, especially in IT treatments become the norm.

Tobias Baer conducts psychological research at the University of Cambridge; he is a a former partner at McKinsey and a member of our Behavioral Insights Group. Vijay D’Silva is a senior partner in McKinsey’s New York office.

79 McKinsey on Payments August 2018 Mapping AI techniques to problem types

As artificial intelligence technologies advance, so does the definition of which techniques constitute AI (see sidebar, “Deep learning’s origins and pioneers”). 1 For the purposes of this paper, we use AI as shorthand specifically to refer to deep learning techniques that use artificial neural networks. In this section, we define a range of AI and advanced analytics techniques as well as key problem types to which these techniques can be applied.

Michael Chui Neural networks and other machine been studied since the 1940s and have re - James Manyika learning techniques turned to prominence as computer process - Mehdi Miremadi We looked at the value potential of a range of ing power has increased and large training analytics techniques. The focus of our re - Nicolaus Henke data sets have been used to successfully ana - search was on methods using artificial neural Rita Chung lyze input data such as images, video, and networks for deep learning, which we collec - Pieter Nel speech. AI practitioners refer to these tech - tively refer to as AI in this paper, understand - niques as “deep learning,” since neural net - Sankalp Malhotra ing that in different times and contexts, other works have many (“deep”) layers of simulated techniques can and have been included in AI. interconnected neurons. Before deep learn - We also examined other machine learning ing, neural networks often had only three to techniques and traditional analytics tech - five layers and dozens of neurons; deep learn - niques (Exhibit 1, page 81). We focused on ing networks can have seven to ten or more specific potential applications of AI in busi - layers, with simulated neurons numbering ness and the public sector (sometimes de - into the millions. scribed as “artificial narrow AI”) rather than In this paper, we analyzed the applications the longer-term possibility of an “artificial and value of three neural network tech - general intelligence” that could potentially niques: perform any intellectual task a human being is capable of. • Feed forward neural networks. One of the most common types of artificial neural Neural networks are a subset of machine network. In this architecture, information learning techniques. Essentially, they are AI moves in only one direction, forward, from systems based on simulating connected the input layer, through the “hidden” lay - “neural units,” loosely modeling the way that ers, to the output layer. There are no loops neurons interact in the brain. Computational in the network. The first single-neuron models inspired by neural connections have network was proposed in 1958 by AI pio -

Editor’s note: This article is a reprint of a chapter from the April 2018 McKinsey Global Institute discussion paper, “Notes from the AI frontier: Insights from hundreds of use 1 For a detailed look at AI techniques, cases.” The full paper can be downloaded here: https://www.mckinsey.com/featured-in - see An executive’s guide to AI, McKinsey Analytics, January 2018. sights/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep- https://www.mckinsey.com/business- functions/mckinsey-analytics/our-insi learning. ghts/an-executives-guide-to-ai.

Mapping AI techniques to problem types 80 Exhibit 1 Artiﬁcial intelligence, machine learning, and other analytics techniques examined for this research.

Techniques Considered AI for this research

Likelihood to be used in Advanced techniques AI applications

More Transfer learning Reinforcement learning Less Deep learning neural networks (e.g., feed forward neural networks, CNNs, RNNs, GANs)

Dimensionality reduction Ensemble learning (e.g., PCA, tSNE) (e.g., random forest, gradient boosting)

Instance based (e.g., KNN) Decision tree learning

Linear classiÞers Monte Carlo Clustering (e.g., k-means, (e.g., Fisher’s linear methods tree based, db scan) discriminant, SVM)

Statistical inference (e.g., Bayesian Markov process Regression Analysis inference, ANOVA) (e.g., Markov chain) (e.g., linear, logistic, lasso)

Descriptive statistics (e.g., conÞdence interval) Naive Bayes classiÞer

Traditional techniques

Source: McKinsey Global Institute analysis

neer Frank Rosenblatt. While the idea is vember 2016, Oxford University re - not new, advances in computing power, searchers reported that a system based on training algorithms, and available data led recurrent neural networks (and convolu - to higher levels of performance than previ - tional neural networks) had achieved 95 ously possible. percent accuracy in reading lips, outper - forming experienced human lip readers, Recurrent neural networks (RNNs). Artifi - who tested at 52 percent accuracy. • cial neural networks whose connections between neurons include loops, well- Convolutional neural networks (CNNs). suited for processing sequences of inputs, • Artificial neural networks in which the which makes them highly effective in a connections between neural layers are in - wide range of applications, from handwrit - spired by the organization of the animal vi - ing, to texts, to speech recognition. In No - sual cortex, the portion of the brain that

81 McKinsey on Payments August 2018 Deep learning’s origins and pioneers

It is too early to write a full history of deep learning—and some of the details are contested—but we can already trace an admittedly incomplete outline of its origins and identify some of the pioneers. They include Warren McCulloch and Walter Pitts, who as early as 1943 proposed an artificial neuron, a computational model of the “nerve net” in the brain. 2 Bernard Widrow and Ted Hoff at Stanford University developed a neural network application by reducing noise in phone lines in the late 1950s. 3 Around the same time, Frank Rosenblatt, an American psychologist, introduced the idea of a device called the Perceptron, which mimicked the neural structure of the brain and showed an ability to learn. 4 MIT’s Marvin Minsky and Seymour Papert then put a damper on this research in their 1969 book “Percep - trons,” by showing mathematically that the Perceptron could only perform very basic tasks. 5 Their book also dis - cussed the difficulty of training multi-layer neural networks. In 1986, Geoffrey Hinton at the University of Toronto, along with colleagues David Rumelhart and Ronald Williams, solved this training problem with the publication of a now famous back propagation training algorithm—although some practitioners point to a Finnish mathematician, Seppo Linnainmaa, as having invented back propagation already in the 1960s. 6 Yann LeCun at NYU pioneered the use of neural networks on image recognition tasks and his 1998 paper defined the concept of convolutional neural networks, which mimic the human visual cortex. 7 In parallel, John Hopfield popularized the “Hopfield” network which was the first recurrent neural network. 8 This was subsequently expanded upon by Jurgen Schmidhuber and Sepp Hochreiter in 1997 with the introduction of the long short-term memory (LSTM), greatly improving the efficiency and practicality of recurrent neural networks. 9 Hinton and two of his students in 2012 highlighted the power of deep learn - ing when they obtained significant results in the well-known ImageNet competition, based on a dataset collated by Fei-Fei Li and others. 10 At the same time, Jeffrey Dean and Andrew Ng were doing breakthrough work on large scale image recognition at Google Brain. 11 Deep learning also enhanced the existing field of reinforcement learning, led by researchers such as Richard Sutton, leading to the game-playing successes of systems developed by DeepMind. 12 In 2014, Ian Goodfellow published his paper on generative adversarial networks, which along with reinforcement learn - ing has become the focus of much of the recent research in the field. 13 Continuing advances in AI capabilities have led to Stanford University’s One Hundred Year Study on Artificial Intelligence, founded by Eric Horvitz, building on the long-standing research he and his colleagues have led at Microsoft Research. We have benefited from the input and guidance of many of these pioneers in our research over the past few years.

2 Warren McCulloch and Walter Pitts, “A logical calculus of the ideas immanent in nervous activity,” B ulletin of Mathematical Biophysics , volume 5, 1943. 3 Andrew Goldstein, “Bernard Widrow oral history,” IEEE Global History Network , 1997. 4 Frank Rosenblatt, “The Perceptron: A probabilistic model for information storage and organization in the brain,” Psychological review , volume 65, number 6, 1958. 5 Marvin Minsky and Seymour A. Papert, Perceptrons: An introduction to computational geometry , MIT Press, January 1969. 6 David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams, “Learning representations by back-propagating errors,” Nature, volume 323, October 1986; for a discussion of Linnainmaa’s role see Juergen Schmidhuber, Who invented backpropagation?, Blog post http://people.idsia.ch/~juergen/who-invented-backpropagation.html, 2014. 7 Yann LeCun, Patrick Haffner, Leon Botton, and Yoshua Bengio, Object recognition with gradient-based learning, Proceedings of the IEEE, November 1998. 8 John Hopfield, Neural networkds and physical systems with emergent collective computational abilities, PNAS, April 1982. 9 Sepp Hochreiter and Juergen Schmidhuber, “Long short-term memory,” Neural Computation, volume 9, number 8, December 1997. 10 Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, ImageNet classification with deep convolutional neural networks, NIPS 12 proceedings of the 25th International Conference on Neural Information Processing Systems, December 2012. 11 Jeffrey Dean et al., Large scale distributed deep networks, NIPS 2012. 12 Richard S. Sutton and Andrew G. Barto, Reinforcement learning: An introduction, MIT Press, 1998. 13 Ian J. Goodfellow, Generative adversarial networks, ArXiv, June 2014.

Mapping AI techniques to problem types 82 processes images, well suited for visual niques can be applied to solve real-life prob - perception tasks. lems. For this research, we created a taxon - omy of high-level problem types, We estimated the potential of those three characterized by the inputs, outputs, and pur - deep neural network techniques to create pose of each. A corresponding set of analytic value, as well as other machine learning tech - techniques can be applied to solve these niques such as tree-based ensemble learning, problems. These problem types include: classifiers, and clustering, and traditional an - alytics such as dimensionality reduction and Classification. Based on a set of training regression. • data, categorize new inputs as belonging to one of a set of categories. An example of For our use cases, we also considered two classification is identifying whether an other techniques—generative adversarial net - image contains a specific type of object, works (GANs) and reinforcement learning— such as a truck or a car, or a product of ac - but did not include them in our potential value ceptable quality coming from a manufac - assessment of AI, since they remain nascent turing line. techniques that are not yet widely applied in business contexts. However, as we note in the Continuous estimation . Based on a set of concluding section of this paper, they may • training data, estimate the next numeric have considerable relevance in the future. value in a sequence. This type of problem is sometimes described as “prediction,” par - Generative adversarial networks (GANs). ticularly when it is applied to time series • These usually use two neural networks data. One example of continuous estima - contesting each other in a zero-sum game tion is forecasting the sales demand for a framework (thus “adversarial”). GANs can product, based on a set of input data such learn to mimic various distributions of as previous sales figures, consumer senti - data (for example text, speech, and images) ment, and weather. Another example is and are therefore valuable in generating predicting the price of real estate, such as a test datasets when these are not readily building, using data describing the prop - available. erty combined with photos of it. Reinforcement learning. This is a subfield Clustering. These problems require a sys - • of machine learning in which systems are • tem to create a set of categories, for which trained by receiving virtual “rewards” or individual data instances have a set of com - “punishments,” essentially learning by trial mon or similar characteristics. An example and error. Google DeepMind has used rein - of clustering is creating a set of consumer forcement learning to develop systems that segments based on data about individual can play games, including video games and consumers, including demographics, pref - board games such as Go, better than erences, and buyer behavior. human champions. All other optimization. These problems Problem types and the analytic techniques that can be applied to solve them • require a system to generate a set of out - In a business setting, those analytic tech - puts that optimize outcomes for a specific

83 McKinsey on Payments August 2018 Exhibit 2 Problem types and sample techniques.

Essential Relevant

Total AI value potential that could be unlocked by problem types as essential versus relevant to use cases Problem types Sample techniques %

ClassiÞcation CNNs, logistic regression 44 29 72

Continuous Feed forward neural networks, estimation linear regression 37 29 66

Clustering K-means, afÞnity propagation 16 39 55

All other Genetic algorithms optimization 17 21 37

Anomaly One-class support vector machines, detection k-nearest neighbors, neural networks 19 6 24

Ranking support vector machines, Ranking neural networks 98 17

Recommender Collaborative Þltering systems 14 15 1 Data Generative adversarial networks generation (GANs), hidden Markov models 7 7 0

NOTE: Sample techniques include traditional analytical techniques, machine learning, and the deep learning techniques we describe in this paper as AI. Numbers may not sum due to rounding. Source: McKinsey Global Institute analysis

objective function (some of the other prob - vibration data associated with the per - lem types can be considered types of opti - formance of an operating piece of machin - mization, so we describe these as “all other” ery, and then determine whether a new optimization). Generating a route for a ve - vibration reading suggests that the ma - hicle that creates the optimum combina - chine is not operating normally. Note that tion of time and fuel use is an example of anomaly detection can be considered a optimization. subcategory of classification.

Anomaly detection. Given a training set Ranking. Ranking algorithms are used • of data, determine whether specific inputs • most often in information retrieval prob - are out of the ordinary. For instance, a sys - lems in which the results of a query or re - tem could be trained on a set of historical quest needs to be ordered by some

Mapping AI techniques to problem types 84 criterion. Recommendation systems sug - ular style, after having been trained on gesting next product to buy use these types pieces of music in that style. of algorithms as a final step, sorting sug - Exhibit 2 (page 84) illustrates the relative gestions by relevance, before presenting total value of these problem types across our the results to the user. database of use cases, along with some of the Recommendations. These systems pro - sample analytics techniques that can be used • vide recommendations, based on a set of to solve each problem type. The most preva - training data. A common example of rec - lent problem types are classification, contin - ommendations are systems that suggest uous estimation, and clustering, suggesting the “next product to buy” for a customer, that meeting the requirements and develop - based on the buying patterns of similar in - ing the capabilities in associated techniques dividuals, and the observed behavior of the could have the widest benefit. Some of the specific person. problem types that rank lower can be viewed as subcategories of other problem types—for Data generation. These problems require example, anomaly detection is a special case • a system to generate appropriately novel of classification, while recommendations can data based on training data. For instance, a be considered a type of optimization prob - music composition system might be used lem—and thus their associated capabilities to generate new pieces of music in a partic - could be even more relevant.

Michael Chui is a partner in McKinsey’s San Francisco office, where James Manyika is a senior partner. Mehdi Miremadi is a partner in the Chicago office, Nicolaus Henke is a senior partner in the London office, and Rita Chung is a consultant in the Silicon Valley office. Pieter Nel is a digital expert in the New York office, where Sankalp Malhotra is a consultant.

85 McKinsey on Payments August 2018 Data sheet: Advanced analytics The size of the prize in banking as data and analytical tools and use cases proliferate is significant, and leaders are pulling ahead.

Advanced analytics could be worth $0.5-1 trillion Big data and artiﬁcial intelligence/machine learning ventures receive most VC to banks globally, representing 8-14% of revenue. investment outside of SaaS. $ annually $ million, 2017

No. of companies1

Software as 19,197 1983 a service (SaaS)

Big data 7,540 867 Marketing & Sales ArtiÞcial intelligence/ 6,812 1021 $0.30T - $0.6T machine learning Risk Cyber security 3,629 326 $0.2T - $0.6T

Internet of things 1,945 365

188 Virtual reality 1,256

105 Augmented reality 948 Corporate Operations <$0.1T <$0.1T In total, ~3400 Þrms received $28.5 billion funding in 2017

1 Number of companies which have received funding in 2017. Some of the startups are classiÞed in more than one vertical. Source: PitchBook; SILA (Startup and Investment Landscape Analytics) leverages McKinsey databases covering more than 1.7 million companies, to signiÞcantly accelerate M&A target scans in a wide variety of Source: McKinsey Global Institute industries and sub sectors globally.

Banks are increasingly deploying more sophisticated Analytics leaders1 exhibit analytics use cases in early stages, but not yet at scale. stronger ﬁnancial performance Percent of banking respondents deploying use case at scale than other companies Analytics maturity correlation to More mature financial metrics Credit risk underwriting AQ score1 Top quartile Transactional analytics AQ companies Multichannel segmentation Bottom quartile Next product to buy 3.4 AQ companies Retention 5-year Digital marketing revenue Cybersecurity CAGR Fraud % Collections 1.4 Pricing optimization AML High AQ score Cash management correlates to sustained Þnancial Portfolio optimization and NPL analyser outperformance Client acquisition AQ score (5+ years) Trading analytics Advanced Þnancial analytics Branch optimization 5.1 Capital optimization Early-warning systems 5-year Partnership marketing EBITA CAGR IT cost optimization % Predictive HR 2.1 Conduct risk Product design (e.g., bundling, new products) Productivity optimisation Fee leakage 1 Analytics Quotient (AQ) is a McKinsey solution developed in 2017 to standardize the measure of analytics maturity across sectors. The AQ MROI optimization survey has been taken by over 120 companies, across industries worldwide. Analytics leaders are deÞned as the top quartile of More nascent companies as determined by overall AQ score.

Source: McKinsey Analytics Quotient Source: McKinsey Analytics Quotient

86 Geospatial analytics, now indispensable in the retail industry, will become one of the critical drivers in consumer banking.

Tokyo residents with similar purchasing behaviors shop in a few Some of the smallest transportation corridors by traffic volume speciﬁc areas of the city have most potential for high-end transportation services, given Demographic and income data were inferred by analyzing where mobile commuters socio-economic profile devices are stationary at night

McKinsey Geospatial team analysis using Factual's point of interest and mobile device location data McKinsey team analysis based on commuting ßow and demographic data from the US Census

Foot traffic, dispersed across the city during work hours, becomes Luxury retailer identified store specific halo effects on web sales much more concentrated in one “special ward” (municipality) of based on store and online transaction data, by considering the Tokyo in the evening effects of proximity to target demographic, competitor stores and foot traffic

Tokyo during work hours Tokyo after work hours Weekday between 10AM – 11AM Weekday between 10PM – 11PM

McKinsey Geospatial team analysis using Skyhook`s mobile location data and visualized with McKinsey's OMNI Solution blends online activity and in-store sales along with demographics and Kepler.gl mobile device location data.

Banks improving intelligence of their omnichannel coverage models

Many banks have a sub-optimal branch footprint, as a result of There are substantial behavioral differences between customer legacy business decisions segments, even within the same neighborhood, which drive their choices of branch visits and online channels

Source: McKinsey Retail Branch Geospatial Optimizer Tool provides an advanced analytics solution to retail banks focused on transforming the branch network into a high-value, high-functioning operation, by utilizing digital and ofßine data.

87 McKinsey on Payments August 2018