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Artificial Intelligence Will Continue to Transform Modern Credit Management

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Artificial Intelligence Will Continue to Transform Modern Credit Management Artificial Intelligence Will Continue to Transform Modern Credit Management: Can We Handle It? Anthony J. Scriffignano, Ph.D. SVP/Chief Data Scientist Dun & Bradstreet @Scriffignano1 A little context before we get started… Are we using our technology to make things better, or merely to make things different? Is life getting easier, better, or just more complicated Are we losing touch with our technology? 2 CURIOUS TIMES A REFLECTION ON WHAT IS HAPPENING IN THE WORLD OF ALL THINGS DATA AND AI TODAY MAKING OLD DECISIONS IN NEW WAYS HOW WE ARE ABLE TO USE MODERN DATA AND TECHNOLOGY TO ANSWER NEW QUESTIONS IN THE TODAY CONTEXT OF BUSINESS DECISIONS LOOKING AHEAD A LOOK AT SOME OF THE FUTURE DISRUPTIONS IN TECHNOLOGY AND WORKFORCE AND SOME ADVICE ON HOW TO GET READY 3 Getting Started: reflecting on credit decisions, now and then… How much has the fundamental business problem changed? How is it likely to change with new data and capabilities? 4 It is possible that we no longer truly understand how information is created and consumed. Not too long ago… Reading about events that happened in the past, listening to people who are not present. Reading about things in the “now” – can’t tell who is communicating with whom – Today… does anybody really know what is going on? 5 Why do we believe what we believe? Too much data? Different objectives? 6 How data is being discovered and used is a constantly-changing environment with changing context. Situational awareness… Unstructured • Far more data is being Data created than used • New types of business beget new types of data, which begets new methods • Commonly available tools and solutions only address a small part of this space 7 Myths / Inconvenient Truths: More Data is better Data vs. noise Data at rest vs. Put data in 1 place data in motion AI can find answers AI methods have preconditions Machine learning will Regression vs. find hidden truth unprecedented change Natural language processing removes all Language is constantly language barriers changing Real‐time analytics Data collected a second are best ago is not 1‐second old! Mature industries, such as financial services, and evolving industries, such as FinTech, are all disrupted by new technology 9 The Global Fintech Landscape Reaches Over 1000 Companies, $105B In Funding, $867B In Value Source: Forbes, 28-Sept 2016 report Successful FinTech Companies by industry Personal Finance: Envestnet, Yodlee, Mint, Credit Karma, LearnVest, NerdWallet, Personal Capital, Motif, Wealthfront Lending: Prosper, Lending Club, OnDeck, Kabbage, Funding Circle, CommonBond, SoFi, Affirm, Avant Payments: Paypal, Klarna, Abyen, Braintree, Mozido, Square, Venmo, Stripe Money Transfer/Currency: Xoom, Payoneer, TranferWise, WorldRemit, Coinbase, Circle Crowdfunding: Indiegogo, Kickstarter, GoFundMe, CircleUp, Tilt Source: The New York Times, "Ranking the Top Fintech Companies" 6‐April, 2016 Source: Nathan Pacer, VP at Venture Scanner At:https://www.nytimes.com/interactive/2016/04/07/business/dealbook/The‐ at:https://www.slideshare.net/NathanPacer/venture‐scanner‐fintech‐report‐q1‐2017 Fintech‐Power‐Grab.html Why would we assume it’s all for the good? The Jetsons image is licensed under Creative Commons 11 11 Cost of Business & Occupational Fraud The Association of Certified Fraud Examiners Report to Nations on Occupational Fraud and Abuses: “… with “Asset misappropriation by far the most common form of occupational fraud, occurring in more than 83% of cases…” If applied to the estimated Gross World Product, this translates to a potential projected global fraud loss of nearly $3.7 trillion ACFE More than 250,000 Micro & Small Business identity theft businesses were victims of unauthorized caused $268 million in transactions against their business damage in 2016, up from $122 million in 2015. account, with losses totaling $3.1Billion. IRS Javelin LLC 12 So what kind of business fraud are we talking about? BUSINESS-TO-HIGH RISK & FRAUD Trade –(AR & AP) Bust out Mortgage Business Id Theft Healthcare – Financial Statement Medical Payment / Trade-Ring Embezzlement Business Credit Coach Tax Evasion Money Laundering Ponzi Wire Fraud Mail Fraud Bribery Forgery Conspiracy Shell / Shelf Corp Public Corruption EMERGING Cyber Cryptoeconometrics Convergence (e.g. Iot and Crypto) Unexplainable AI Fake news Dark Web ACFE Silent Data Breach 13 Understanding traditional types of malfeasance does not guarantee understanding of new behaviors Business Identity Theft Place Orders as an agent Representing Various Delivery Points Impersonating Large VICTIM COMPANIES Impersonated Business - Identity Theft Victim Commercial Business Entities Walk-In Freezer Television Two Men Charged With $900,000 Computers Interstate Theft Scheme NEWARK, N.J. - Two New Jersey men Microwave were arrested today for their roles in a scheme to fraudulently obtain more than $900,000 in commercial and residential Plasma Cutters merchandise from various companies, Acting U.S. Attorney William E. Fitzpatrick Commercial Ice Maker announced. Roy Depack, a/k/a “Ray Depack,” a/k/a “Roy Soriano,” a/k/a “John Soriano,” 42, of Digital Scales Elizabeth, New Jersey, and Louis J. Pobutkiewicz Sr., 39, of Newark, are 100+ Different Power Tools charged by complaint with conspiracy to Phone commit mail and wire fraud. Numbers ETC. Single Family House, Apartments Retail Stores, Restaurants 14 The future of credit management is in part up to us to write… Geo/Device Identification Bot/Automated attacks Behavior/Activity analysis Smarter human attacks Advanced anomaly detection Data Breach Control vs. Audit/Trust IP theft Federation of AI Adversarial interventions CURIOUS TIMES A REFLECTION ON WHAT IS HAPPENING IN THE WORLD OF ALL THINGS DATA AND AI TODAY MAKING OLD DECISIONS IN NEW WAYS HOW WE ARE ABLE TO USE MODERN DATA AND TECHNOLOGY TO ANSWER NEW QUESTIONS IN THE TODAY CONTEXT OF BUSINESS DECISIONS LOOKING AHEAD A LOOK AT SOME OF THE FUTURE DISRUPTIONS IN TECHNOLOGY AND WORKFORCE AND SOME ADVICE ON HOW TO GET READY 16 17 With machine learning anything is possible. That is sometimes part of the problem… 18 The definitions, objectives and methods of “Artificial Intelligence” continue to evolve • Mimic human behavior ……or loosely approximate the mental models • Mimic thinking • Project human intent • Advise humans • Behave intelligently • Behave rationally • Behave empathetically …explicitly model the human brain Multistate Markovian Survival Model Bayes’ Network of perfume characteristics Architecture of the “projectome” Network model of ‘target’ and ‘source’ nodes via anatomical tracing 19 Learning about learning Best Practices: • Don’t lead with a method • Understand preconditions • Understand character of data • Mixed methods • Controls for stewardship • Controls for stability of approach • Dispositive threshold Thinking about Artificial Intelligence What do you have to believe? Can you vs. may you use information “Unlearning” Veracity adjudication Provenance / decision synthesis Recreating prior conditions for forensics Thinking about Partner 1 Customer 1 Cloud relationships Cloud Some examples • Supply Chain / Integrated Value Chain Ink Blot, City 1 • Understanding Signals derived from changes to business information Public data • Discovering and investigating clusters of D1.1, D1.2, Cloud DUNS unusual behavior D1.3,…D1.n • Exploring the impact of new regulation (e.g. privacy) Customer 2 Cloud • Understanding intra‐regional opportunities (e.g. cross‐border) • Exploring the impact of new market forces (e.g. Brexit) Partner 2 • Studying the real or potential impact of Cloud supply chain interruptions (e.g. disasters) • Investigating emerging capabilities (e.g. reputational risk) Ink Blot, City 2 • Malfeasance DUNS D1.1, D1.2, D1.3,…D1.n 22 Black Cat problems… Dealing with “Black Cat” problems • Signals – internal / external • Nodes / edges • Systemic measures – quality, character • Anomaly detection / Graph inspection Anisotropism Betweenness centrality Hierarchical simplification / Sparse graph analysis Cliques Clustering coefficients • Data sensing – new sources / uses • Triggers – events, observations 23 The use of: data science tools, machine learning/artificial intelligence are keys our success to mitigate B2B malfeasance 24 CURIOUS TIMES A REFLECTION ON WHAT IS HAPPENING IN THE WORLD OF ALL THINGS DATA AND AI TODAY MAKING OLD DECISIONS IN NEW WAYS HOW WE ARE ABLE TO USE MODERN DATA AND TECHNOLOGY TO ANSWER NEW QUESTIONS IN THE TODAY CONTEXT OF BUSINESS DECISIONS LOOKING AHEAD A LOOK AT SOME OF THE FUTURE DISRUPTIONS IN TECHNOLOGY AND WORKFORCE AND SOME ADVICE ON HOW TO GET READY 25 We live in an age of promise. Big data and advanced methods are making things possible that were science fiction only a few short years ago. Unintended use is a serious consideration in the rush to market products and services Massive connectivity is also increasing the risk of being “wrong” The state of all things AI also continues to evolve Source: https://www.forbes.com/sites/gilpress/2017/01/23/top‐10‐hot‐ artificial‐intelligence‐ai‐technologies/#10eb1f7c1928 Truth be told, it’s overwhelming and more than a bit unstable… It will never be simpler. 30 31 We must continue to challenge our perspectives and our beliefs New Behaviors Changing Perspectives Complex Issues New Opportunity 32 And what about the next generation? In the past, we looked for… • Data Curator • Analyst Now, we need all of that • Modeler and more! • Statistician • Methodologist • Coder • Governance Expert • Problem Formulator • Detective • Visionary
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