WHITE PAPER
BLOCKCHAIN TECHNOLOGY AND THE FINANCIAL SERVICES MARKET RETHINKINGState-of-the-Art CREDIT Analysis LENDING
Abstract Lending industry’s dynamics are changing rapidly. A whole new breed of lenders are fundamentally transforming the industry and redefining its modus operandi. Most Financial Institutions (FIs) are still doing things the traditional way and grappling with the challenges of legacy systems, outmoded processes, and inertia to change. To remain relevant, the lending groups within FIs need to respond quickly to meet emerging challenges. This paper examines the key drivers for the financial institutions to reinvent themselves, key market trends in the current lending environment, how financial institutions are standing up to the challenges, some of the innovations in the financial industry and their disruption potential, and lastly this paper recommends what FIs can do to not just subsist but also flourish in this environment. The contents herein are equally applicable to both retail lending as well as small ticket business lending. Contents Introduction 03
Business imperatives for change 04
How is the industry responding? 05
Customer centricity 05 Advanced analytics 05 Operational efficiency 07
‘Out-of-the-box’ ideas 09
What can Financial Institutions do? 10
Response Approaches 11
A word of caution 12
Final thoughts 13
How can Infosys help? 14
References 16
External Document © 2018 Infosys Limited Introduction Introduction For borrowers, lending has traditionally collection, non-digitized data, manual catalyzing lending space by simplifying meant filling up long application forms, and subjective underwriting processes, borrowing experience, and faster time to submission of volumes of documents, data spread across files and systems, decision leveraging alternate sources of some of which are not even readily and consequently less than optimum data (from E-Commerce, UPS to payables available which meant spending long decisions. and card network data) to sell better hours preparing them, several back and and manage credit risks better. With P2P In this age of consumer, the pressure is on forth with lenders, followed by months lending through platforms expected to long due diligence with some obscure financial institutions to keep consumers grow to around $350 billion, and business methods with no status updates, and constantly happy with speed and lending through online platforms finally decision with no justifications convenience, while dealing the internal expected to reach around $206 billion and sometimes too late to be of any use. operational challenges and regulatory by year 2025, only in the USA1, there is For lenders, this was characterized by compliance. While large financial merit in examining the environment and huge data needs for underwriting, weeks institutions are grappling with inertia defining a charter for reformation. long data collection, experiential data to change, nimble technology firms are
External Document © 2018 Infosys Limited Business imperatives for change The key imperatives that are driving FIs to reform are:
Make it Help me Convenient Trust you and easy for me
Know me
Rising consumer Disruptions from Increasing regulatory Operational expectations alternative lenders scrutiny challenges Figure. 1
1. Rising consumer expectations Securities and Exchange Commission’s 3. Increasing regulatory requirements With so many providers to choose (SEC’s) approval in 2008. With end-to-end Regulations are becoming more stringent from, consumers have become more technology-based operations, alternative with: demanding. They no longer need the lenders are offering all the convenience • New data and reporting standard ‘product’ offered by providers. and speed consumers need and have requirements Rather, they need ‘solutions’ for their become the first choice for small ticket • Pressure to invest in IT infrastructure problems, which translate into needs. loans. They can be classified as: For their credit needs, consumers are • Early detection of Capital at Risk asking for: • Online marketplace lenders (P2P and (CaR) balance sheet lenders): P2P lenders • Easy and convenient lending 4. Operational challenges do not lend but connect lenders and experiences with speedy fund While the factors described above are delivery borrowers using a fee-based model. external to an organization and beyond Lending Club, Funding Circle, and • Personalized services with implicit their control, operational challenges are assumptions that providers Prosper fall into this class of lenders. internal and can be prevented. Firms remember them from past Balance sheet lenders are lenders in typically employ long, manual, and interactions the true sense of the term because subjective underwriting processes. They • Utmost transparency in the process they hold debt within their own use multiple systems for processing and and assurance of safety balance sheets. Kabbage, OnDeck, data is spread across systems with no and Can Capital are few examples of single source of truth. Such firms face 2. Disruptions from alternative lenders such lenders. challenges related to: Alternative lenders mushroomed in 2005 • Legacy systems • Small Business Administration (SBA) with the launch of Zopa and gained backed online lenders e.g. SmartBiz • Process inefficiencies attention in 2007 with Lending Club • Data quality and integration receiving major funding and subsequent
External Document © 2018 Infosys Limited How is the industry understanding of the potential uses of identify solutions, and providing responding? technology. The actual steps taken by advice while engaging customers. In FIs can be classified under three broad the current environment, where the The response of FIs to the changing categories: offerings of various providers have little lending landscape has primarily been differentiation, “the only sustainable 1. Customer centricity two-fold. First is keeping customers competitive advantage left is an 2. Advanced analytics at the center of the organization and obsession with customers and the key to designing everything else around them. 3. Operational efficiency growth is customer loyalty”2. Therefore, The second is a substantial increase in the first step is providing customers with usage and deployment of technology- 1. Customer centricity a superior experience as well as to ensure based solutions on all fronts. This has Customer centricity entails building a that customers trust the brand. This leads been fueled by both the advancement customer’s trust in the brand, moving to customer loyalty and advocacy, which of technology in automation and data away from the concept of a ‘product’ in turn become the source of competitive warehousing, and by an increased to a ‘solution mindset,’ helping clients advantage.
Customer Experience Customer Customer Competitive
Loyalty Advocacy Advantage Customer Trust
Figure. 2
Towards customer centricity, firms are • Enabling reviews and ratings by are being used to identify non-linear making significant efforts on two fronts: creating consumer communities and relationships between variables. Analytics building customer trust and augmenting forums on websites. This also helps is being leveraged to offer advisory customer engagement. in building the brand as consumers capabilities based on customer data transform into brand ambassadors analytics to better understand customer Customer trust by solving the problems of other needs and create ‘sticky’ relationships. Firms are investing in building customer consumers using their own Specifically, advanced analytics is being trust by: experiences used for:
• Proposing what is best for the Customer engagement customer by evaluating customer Firms are providing free customer Enhancing Customer profiles and needs, and providing engagement tools on their websites for Engagement best product / pricing options customers to ‘play with’ without actually Infosys Case Study • Providing visibility into the selling. Customers are welcome to use the A large UK-based financial institution underwriting process by explaining it tools on the website unconditionally and enhanced customer engagement by in detail and being upfront about the help them solve their business problems. offering the following two interactive eligibility criteria For providers, these tools are helpful tools on its website. • Providing transparency in pricing in two ways: firstly, they provide rich by, for example, creating sliders on insights about potential customers and 1. Working Capital Profiler: a website where pricing is provided secondly, these tools are designed to lead Allows the customer to have upfront for every combination of customers into an optional sales funnel a graphical representation of loan amount and duration and thus create sales opportunities. their working capital and cash • Holding value-based dialogues requirements. Once profiled, this with customers without attempting 2. Advanced analytics information provides the key to create tailored offerings to sell on every occasion. An Assimilation of large volumes of data, example here would be publishing 2. Working Capital Finance Finder: the ingestion and combination of newer informational videos, blogs, and types of data with traditional data, and Allows customers to generate a articles from experts on websites to usage of advanced analytical techniques range of funding options that meet help customers manage spending or for deeper insights, are becoming core their requirements. They can then articles on how to manage working to credit risk management. Machine request a quote for an option they capital for commercial customers have selected learning techniques, like neural networks,
External Document © 2018 Infosys Limited Decisioning ‘thin-file’ customers regular intervals that resemble LinkedIn), web clickstream, location, The lending industry has so far focused repayment cycles used by many shopping, etc. It does not have a direct lenders. Given that, supplier payment primarily on ‘thick-file’ customers. These correlation with credit data but is used data can be used as a proxy for both are customers with sufficient credit data as a proxy for credit worthiness of the ability and willingness to repay loan. to feed the credit models and includes borrower in a non-linear relationship. their credit account information and VantageScore credit scoring model, This data becomes useful in the developed jointly by Equifax, Experian, financial statements. A person with no absence / deficiency of credit data. credit record or a sparse credit report and Transunion; uses conventional alternative data for credit scoring, in The reason it is experimental is that (‘thin file’) will often not receive a credit addition to traditional credit data. the efficacy of such data to assess true score based on traditional models. credit worthiness of borrowers is yet to Led primarily by new-age financial 2. Experimental alternative data be proven. technology (aka FinTech) firms, alternative This is data from sources such as social data sources are now being considered media (Facebook, Twitter, to underwrite ‘thin file’ customers. This alternative data is over and beyond the traditional credit data and is used as a Conventional alternative data proxy for credit worthiness. This type of Data from data can be classified as: Service providers 1. Conventional alternative data Public Credit worthiness is fundamentally Utility bill records payments the ability and willingness to pay. It is assessed on the basis of how the borrower has performed on loan Alternative repayment in the past. Conventional Data alternative data is the data that closely resembles the repayment data. It is the Social Supplier data related to repayment of bills of media data data utilities, suppliers, and other services. Payments for services has a strong correlation with payments of loan Clickstream data installments, both being obligations Experimental on beneficiary and especially since alternative data service suppliers bill their customers Figure. 3 typically on a monthly basis or at
Supplier data features, all serve as useful predictors Public records Suppliers maintain history of payments of post-application behavior, including • Government and regulatory reports; of their business customers, which can possible future attrition4. for example, government contracts and be used as a proxy for the ability and • Marketplace lenders have found that construction index willingness to repay, in addition to the longer a person spends sliding • Public data; for example, business estimating revenue flow and working the loan amount and pricing level licenses, Sec of State data, health capital. (online lenders have started providing inspection reports, police reports, etc. • A McKinsey study discovered that the innovative sliders on the website for • Industry association data; for example, contractual terms and resulting cash loan amount and durations), the more American Medical Association, bar flows between small businesses and likely it is that they will pay back11 associations, etc. their key suppliers were extraordinarily predictive. With a Gini coefficient of 35, Social media data Utility bill payment this factor compared favorably with the • Location data from personal social • An Asian lender found that best variables offered by developed- network and E-Commerce sites to delinquencies on mobile-phone bills market credit bureaus (Gini coefficients validate occupancy and stability of were 60% more predictive of small- for which typically range from 25 to 45)3 residency10 loan defaults than delinquencies on loans from other lenders. Even Clickstream data • Customer reviews to understand the choice of payment plan for the popularity of businesses10 Websites visited prior to filling online loan phone bill was found to be just applications, the nature of the affiliate • Network of people with high-income as predictive as the second-best partner driving the traffic, the amount jobs, the potential for high-income jobs variable available from the credit of time spent on related pages, such or stable employment, in addition to bureau [sic]3 as terms and conditions and product other data to understand job stability10
External Document © 2018 Infosys Limited Enhancing credit scoring models • First Access predicates risk-scoring Recently, some unconventional statistical Examples products for lenders in emerging methods are being incorporated in • ZestFinance uses 70,000 data markets, predominantly on prepaid analytics engines to enhance credit signals — everything from mobile phone usage12 scoring models. Even though these are financial information and • A Latin American consumer lender in investigational stage, the results are technology usage — to gauge found that analyzing recharge promising and include: how quickly a user scrolls behavior—the pattern in which through its terms of service and consumers top up their prepaid • Kalman filters: Initially used for ten, parallel, machine learning mobile phone account—more in-course adjustments to a rocket’s algorithms to assess loans. than doubled its predictive power, trajectory after experiencing • Fundation, an online business compared with the use of simple atmospheric turbulence, it identifies lender using proprietary analytics, 3 the best predictor, given the old and demographic data alone [sic] provides real-time, interactive new data. The baseline scoring model feedback to the customers Data from service providers is updated dynamically with new data on their credit profile as they coming in from successive months, like • Intuit is trialing with a platform gradually provide information in 4 where they use (with the permission payment pattern and suppliers the credit application interface, of small businesses) QuickBooks • Singular value decomposition: Is so that customers can exactly data, which they store in the cloud, a factorization of a real or complex understand how the application is to create a predictive, small business, matrix. It has many useful applications being evaluated. credit score in signal processing and statistics. Used of process steps, and automation of • Alibaba has created a credit score to turn hundreds of tracking variables it calls “Ali-loan,” derived from the representing behavior over time into an business rules with rule engines, is the transactions conducted on its portal. individual ‘DNA’ for each borrower4 general direction of the industry. In It then sells this score to lenders. With addition, lenders are striving to simplify a bad-loan rate of just 0.35 percent, • Ensemble: Allows each model to solve the credit application process. this data source is something that the portion of the problem for which it Reduction in ‘asks’ from customer makes even developed-market is best-suited. Used to blend together a lenders envious3 range of credit scoring models, such as Reduction in ‘asks’ from customers is • Fundbox and Bluevine underwrite logistic regression, decision trees, and on two fronts: firstly, reduction in the 4 customers simply by evaluating their artificial neural networks amount of information itself required for QuickBooks, Xero, or FreshBooks data • Weight of evidence: Provides flexible underwriting and secondly, reduction in tools to recode values in continuous the amount of information to be provided and categorical predictor variables into by customer or fetching the information directly from other sources, without Beyond alternative data, the owned discrete categories automatically and bothering the customer. proprietary data — information as assigns each category with a unique simple as the balances and transaction weight. It also easily incorporates new Alternative lenders have significantly patterns of existing customers’ past predictive information – like a change reduced ‘asks’ on both fronts. Many small relationships — which was ignored due in payment patterns or a riskier use of a businesses are willing to pay the higher to non-availability in digital format, is credit line – without requiring an entire price of the alternative lenders just to be 4 now helping in better assessment of model rebuild able to get their capital and move on, credit worthiness of customers through • Adaptive Control Systems (ACS): rather than spend days and weeks just for digitization. ACS brings consumer behavior and credit application initiation. other attributes into play for decisions Lenders like Kabbage and Iwoca in key management disciplines Example offer simple three or four step credit (line management, collections, and application process with an emphasis One successful lender found that it authorizations), so as to reduce credit on speed and convenience, which actually already had paper records losses and increase promotional takes not more than 30 minutes to of interactions with some small opportunities. It uses behavioral scoring complete. Most such lenders have simple, businesses it wanted to target; it for continuous monitoring of risks at straight forward, data and document had overlooked the records because the individual and portfolio level and requirements, which are displayed they were not in a digital format. adjustments to exposures, pricing, and upfront on websites; thus, preventing A team manually entered the data terms and conditions. TRIAD, developed the hassle of multiple rounds of data into an electronic system, enabling by Fair Isaac, is one such ACS5. collection. the provider to conduct the analysis 3. Operational efficiency required to identify qualified SmartBiz, an online SBA loan provider, borrowers3 Straight-through processing, enabled forgoes the traditional business plan and by simplification and standardization income projections, thereby reducing
External Document © 2018 Infosys Limited the load of data requirements from Process re-engineering and and data breaks through exception borrowers. automation management On the direct data collection front, some None of the above would make sense • Pre-approve credit limits supported firms are using API-based integrations unless the processes are re-engineered by automated underwriting rules to automate collection of customer to reduce processing time, eliminate • Provide visibility into decision information from either the accounting redundancies, reduce errors, and avoid timeline (move away from SLA to systems of customers directly, or from reiterations. To that end, FIs are: customer waiting time) the service providers of the customers. • Create system functionality to allow All that is required of customers is to 1. Modernizing processes changes to the request without share credentials and links to their service • Fetch client data from archives reprocessing deal providers. wherever there is no change • Capture errors upfront in the 2. Deepening automation: process • Early warning systems to • Capture data tailored to the type of monitor the internal and external request (system guided) environment and generate triggers for re-evaluation Examples • Develop system-triggered need for valuation, bureau check, and title • Event-based triggers with an events • Kabbage allows customers to link search library, based on internal and external assessments any of the following services in • Identify data difference for re- their credit applications: Square, process requests • Automated data extraction using Optical Character Recognition Authorize.Net, Sage, Stripe, • Capture data once and reuse (OCR) from images, electronic Intuit, Quickbooks, Xero, Yahoo, for downstream processing and feeds, or paper documents that Etsy, Amazon, PayPal, eBay, and subsequent requests supports various formats - PDF, XLS, • Identify and eliminate capability Business Checking. The data is TIFF, JPG, email, fax, text; including redundancy across underwriting fetched directly from these service interpretation of footnotes toolsets providers and used for qualifying • Automated financial spreading with • Segment by risk profiles and customers industry-specific normalization to the create standard templates for organization’s required standards • OSMO Data Technology Ltd. underwriting each segment • Automated suggestion of credit connects securely with the • Create role-based workflow to limit for each case by breaking support insight into the full history accounting packages (support down organization-wide risk of previous activity and access for 240+ accounting systems) of exposure limits to deal level and relevant documents and data customers (sales ledger, purchase combining with credit scores • Institutionalize individual ledger, invoice line data, or • Automated generation of loan experience and subjective management accounts - P&L, agreements, contracts, terms and judgments into standardized conditions, and other documents in balance sheet, and cash flow), underwriting templates: Credit pre-defined templates collects data confidentially, and Evaluation Grids6 • Automation of business rules using converts the stored data formats • Capture digital data – self-service rule engines created by each accounting portals for customers to enter data • Automated covenant tracking system into a single, unified form, with support for multiple channels • Automated workflows - queue which is then passed to the client’s • Execute digital contracts with management, case routing, case system (the lender). e-signatures tracking, notifications, and status • Minimize manual reconciliations alerts
Process re-engineering and automation Infosys Case Study
Infosys partnered with a financial utility organization to transform data and document capture leveraging advanced technology by:
• Providing a dedicated user interface for every client to upload data and documents • Using rules engine system automatically to determine the type of documents that are required based on geography, legal entity structure, and KYC norms. Huge library of KYC norms and legal frameworks from all over the world support the rules engine in the background • Developing an interface that prompts user to upload specific documents based on preliminary information entered • Using OCR technology from Recognos Financial to extract data from documents. The ability of the tool improves progressively with an increased number of documents passing through it • Enabling OCR to leverage vast document taxonomy to support document variations (types, formats, layouts) from all over the world
External Document © 2018 Infosys Limited ‘Out-of-the-box’ ideas
There are also some completely unconventional ideas for credit underwriting which are gaining attention. It is needless to say that their efficacies are still to be proven. They include:
1. Psychographic underwriting: EFL has developed a 45-minute psychographic query that gauges a potential borrower’s truthfulness, ethics, and optimism to determine whether that person, relative to other borrowers and potential borrowers, will pay back his loan [sic]7. Here is how the query works:
• The query works on the basic principle of relativism: the more someone feels that other people are, say, untruthful, the more untruthful that person is considered to be. In other words, people justify their actions based on their perception of other people, a sort of ‘everyone steals, so I can steal’ mindset [sic]7
• By combining the psychographic evaluation of EFL with voice verification and voice analysis of VoiceTrust, a biometric / psychographic underwriting process takes shape [sic]7
2. Fitness data in underwriting: This uses FitBit data for credit underwriting. Walkmore’s models claim to show the more people move — tracked through smartphone apps or wearable computing devices — the more likely they are to pay dues on time. This allows fitness data to be crunched alongside more traditional data sources that relate to environment and finance (all the data will be anonymous)8
3. Credit Information Platform: This is a cloud-based patent-pending platform from Credit2B. Here is how it works:
• It is a credit information platform and reporting service that integrates credit bureau data with real-time industry trade reporting, all backed by analysts
• It is based on the ‘Intelligence of the community’, that is a network of thousands of leading credit professionals and credit grantors coming together to share information
External Document © 2018 Infosys Limited What can Financial Institutions do? Although the paper has focused primarily on the credit underwriting part of lending, the concepts can be extrapolated to the whole lending process, which includes sales and prospecting, servicing and risk monitoring, in addition to underwriting. Accordingly, there are reformation opportunities across the entire lending value chain, which FIs can undertake.
Figure. 4
External Document © 2018 Infosys Limited Response Approaches
Financial Institutions are taking various approaches to kick-start their transformation journey. These approaches are collaboration, investment, and brownfield development. Following are the examples of each of these approaches13:
Collaboration Partnered with Kabbage to o er real-time access to capital for customers with small businesses UPS Capital
Partnered with Funding Circle and Assetz Capital to provide alternative sources of nance to small RBS businesses
Citibank and Lending Club, with the help of Varadero Capital, will provide loans worth USD 150 million towards ful lling Citi's community re-investment obligations. Citibank Citi will provide these loans through Lending Club's marketplace platform, utilizing their proprietary borrower scoring. Varadero, will then buy the loans o Lending Club's website through a credit facility from Citi
Kabbage announced licensing of its engine by Kikka Capital. Kabbage will provide the platform to KIKKA CAPITAL enable onboarding, underwriting, and monitoring; while Kikka will manage operations, marketing, funding, and loan servicing
BBVA Compass is teaming with OnDeck to o er business owners short-term loans. BBVA Compass will BBVA Compass market OnDeck loans to its small business banking customers. OnDeck will make the loans and collect repayments
MasterCard will lend its network of acquirers to Kabbage. Kabbage Inc., announced the launch of the MasterCard Kabbage Card powered by MasterCard, which gives businesses the ability to pay for items at point-of-sale with a purchasing card tied to their Kabbage account
Investment
Deutsche Bank Deutsche Bank, Key Bank, and Square1 Bank, jointly committed over USD 130 million of credit KeyBank facilities to OnDeck
Westpac Banking Corporation’s new Venture Capital (VC) fund has taken an equity stake in Sydney-based Westpac P2P lender SocietyOne WELLS FARGO FastPay, a startup o ering credit lines to digital media companies, has raised USD 25 million in new funding from Wells Fargo Capital Finance, plus a combination of debt and equity from SF Capital Group
Credit Suisse Prosper has raised USD 165 million, Series-D, nancing led by Credit Suisse Group AG, a Swiss provider of J.P.Morgan nancial services with participation from J.P. Morgan Asset Management and SunTrust Banks Inc. Asset Management
Spanish banking-giant Santander has set up a USD 100 million venture capital fund to invest in Fintech Santander start-ups globally
USD 80 million credit facility has been committed by Goldman Sachs and Fortress Credit jointly to OnDeck Goldsman Sachs loans for small businesses
Brown eld development
Goldman Sachs is planning to launch Peer-to-peer (P2P) lending platform which will enable it to Goldsman Sachs directly compete with Fintech startups
SunTrust acquired an online lending portal in 2012 to support LightStream — its super-prime, online SunTrust lending platform. LightStream is an entirely online process, where the funds are deposited directly into the borrower’s bank account
One of Israel’s largest banks, Bank Leumi, will launch a new P2P lending service. This may be the rst P2P service from a global bank Bank Leumi Leumi will receive a fee from every P2P loan that it coordinates, and would also be capable of providing underwriting and an educated debt rating on existing customers – but that may not be the point
External Document © 2018 Infosys Limited A word of caution Reporting challenges about the specific reason for an action 9 While adapting to challenges is necessary, In spite of so many years of operation, taken, under Regulation B . FIs need to keep a good eye on the credit bureaus are still struggling to Fair lending challenges regulations to make sure they are not maintain the integrity of baseline credit With proliferation of numerous, new data getting into the cross hairs. data. A large number of consumers types and models for credit scoring, the report about material inaccuracies in The efficacy of credit scoring based on challenge is ensuring that no data point their credit data and file disputes with experimental alternative data is still is discriminatory towards any applicant. bureaus. Imagine this for alternative data. unproven and its usage presents new It is hard to predict what data might be Given the massive volume and the large compliance challenges and uncertainties. discriminatory without knowing how number of sources for procurement, It is not crystal clear how this form of a model evaluates that data. With no maintaining the accuracy and credit scoring will cope with reporting formal guidelines, it is up to the lenders completeness of this data is a challenge requirements and fair lending laws to ensure that they are not in violation of not for the faint-hearted. and the industry has raised concerns ECOA9. over compliance with the Equal Credit With an even more complicated credit Opportunity Act (ECOA) and Foreign profile, an additional complication is Contribution Regulation Act (FCRA). justifying and explaining to consumers
External Document © 2018 Infosys Limited Final thoughts and innovation culture. As expected, expansion of this form of lending. most of the innovations in lending are Many financial institutions have begun Consumers are yearning for a simple, being driven by alternative lenders. to take cognizance of the business straightforward borrowing process Also, what started out as a retail lending imperatives of change and several have and want money in the bank account model has already expanded to small launched full-fledged transformation, instantaneously. While that may be business lending, and is moving or at least piecemeal initiatives. The utopian, alternative lenders have realized towards commercial lending. Even disruptions caused by alternative lenders, the need and are improvising to the though alternative lenders are largely besides presenting challenges, have also extreme to make lending ‘instant’. They unregulated, except at the state-level and opened a plethora of opportunities. It is are in a better position than traditional by Securities and Exchange Commission therefore, worthwhile for FIs to take cues lenders to do so, given their nimble (SEC), imminent regulations may not and rethink credit lending. processes, high technology quotient, change much given the exponential
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External Document © 2018 Infosys Limited Infosys led lending the end-to-end underwriting process transformation at a leading within the organization global financial services firm • Granular level challenges in the current with end-to-end assessment state with priority and business impact of capabilities, building future • Vision of future state capabilities state vision and envisioning supported by appropriate technology architecture that focused on customer product centricity, deepening business insights Faced with operational challenges, long through advanced data analytics, and lead times and difficulty in meeting increasing operational efficiencies customer expectations, the Client • Roadmap highlighting list of strategic wanted to relook its current commercial and tactical initiatives to achieve target underwriting capabilities form functional state and technology standpoint. In the second phase of the engagement In the first phase of the engagement, Infosys helped build the platform to Infosys conducted functional and support the future state vision by: technology assessment of commercial • Identifying the key capabilities / underwriting capabilities, performed features for future state commercial industry benchmarking of commercial underwriting and elaborating the underwriting capabilities and requirements through high level recommended future state. Additionally, business processes and user stories Infosys conducted two day ‘Design • Supporting PMO in development Thinking’ workshop with Client of the business architecture for the organization to build the vision for future development of the Commercial state Underwriting Capability Phase one concluded with: • Coordinating with technology units to • Establishment of a common align on the key components and data understanding of the current state needs and developing sprint plans capabilities and their maturities, across
Contacts : Manishi Varma Rajesh Menon Pudukulangare Senior Principal Partner Financial Services, Infosys Consulting Financial Services, Infosys Consulting [email protected] [email protected]
External Document © 2018 Infosys Limited References 1 “US Alternative Lending Market Report”, GrowthPraxis 2015 2 Forrester: Customer Advocacy 2014: How US Consumers Rate Their Financial Institutions, Bill Doyle, December 4, 2014 3 ”New credit-risk models for the unbanked”, Tobias Baer, Tony Goland, Robert Schiff, McKinsey & Company, March 2012 4 ”Reinventing the Underwriting Paradigm”, Opera Solutions, LLC, 2013 5 FICO® TRIAD® Customer Manager http://www.fico.com/en/products/fico-triad-customer-manager#overview 6 “Credit Evaluation Grids for MicroLenders: A Tool for Enhancing Scale and Efficiency”, By Julie A. Gerschick On behalf of ACCION USA, Feburary 2002 7 Hornblass, J. J. “How Credit Underwriting Can Transform Using Voice Authentication and Psychometrics.” Bank Innovation. N.p., n.d. Web. 12 Nov. 2015.
Author : Key Contributors: Anurag Arora Saikat Pathak
Anurag is a Senior Consultant at Infosys Saikat is a Principal Consultant at Infosys Consulting. He has over 18 years of Consulting. He has consulted for more experience in banking and consulting and has provided advisory services to than six years for financial services clients around risk and compliance, handled multiple business and process firms. During this time, he has helped transformation engagements in the banking domain. Prior to Infosys Consulting, clients in major transformations across he worked with ICICI Group, GE Capital Services, Oracle Financial Services digital marketing, credit underwriting, Software, and Temenos. data strategy, and operational analytics Email: [email protected] with current state assessment, industry Swaminathan Sundaresan benchmarking, capability maturity Swami is a Senior Principal Consultant at Infosys Consulting. He has over 20 years assessment, and building future state of experience in commercial banking delivery and management consulting vision and roadmap strategy. Anurag across process optimization and process modeling, business strategy validation, has completed his post-graduation in presales and solutions development, domain architecture assessment, IT strategy management from IIM Lucknow and roadmap, business strategy validation for trade and supply chain finance, and Email: [email protected] next generation portals and platforms. He has to his credit several thought papers in leading journals including Global finance Creation of go to market alliances Email: [email protected]
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