Artificial Intelligence Will Continue to Transform 

Modern Credit Management:

Can We Handle It?Anthony J. Scriffignano, Ph.D.SVP/Chief Data ScientistDun & Bradstreet@Scriffignano1

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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?

A little context before we get started…

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C U R I O U S T I M E SA R E F L E C T I O N O N W H A T I S H A P P E N I N G I N T H E W O R L D O F A L L T H I N G S D A T A A N D A I T O D AY

M A K I N G O L D D E C I S I O N S I N N E W WAY SH O W W E A R E A B L E T O U S E M O D E R N D A T A A N D T E C H N O L O G Y T O A N S W E R N E W Q U E S T I O N S I N T H E C O N T E X T O F B U S I N E S S D E C I S I O N S

L O O K I N G A H E A DA L O O K A T S O M E O F T H E F U T U R E D I S R U P T I O N S I N T E C H N O L O G Y A N D W O R K F O R C E A N D S O M E A D V I C E O N H O W T O G E T R E A D Y

TODAY

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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?

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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 –does anybody really know what is going on?

It is possible that we no longer truly understand how information is created and consumed.

Not too long ago…

Today…

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Why do we believe what we believe?

Too much data?

Different objectives?

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How data is being discovered and used is a constantly-changing environment with changing context.

Situational awareness…

• Far more data is being 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

Unstructured Data

Myths / Inconvenient Truths:

More Data is better

Put data in 1 place

AI can find answers

Machine learning will find hidden truth

Natural language processing removes all

language barriers

Real‐time analytics      are best

Data vs. noise

Data at rest vs.                 data in motion

AI methods havepreconditions

Regression vs.unprecedented change

Language is constantly changing

Data collected a second ago is not 1‐second old!

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Mature industries, such as financial services, and evolving industries, such as FinTech, are all disrupted by new technology

The Global Fintech Landscape Reaches Over 1000 Companies, $105B In Funding, $867B In ValueSource: Forbes, 28-Sept 2016 report

Source: Nathan Pacer, VP at Venture Scannerat:https://www.slideshare.net/NathanPacer/venture‐scanner‐fintech‐report‐q1‐2017

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, 2016At:https://www.nytimes.com/interactive/2016/04/07/business/dealbook/The‐Fintech‐Power‐Grab.html

1111The Jetsons image is licensed under Creative Commons

Why would we assume it’s all for the good?

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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 trillionACFE

More than 250,000 Micro & Small businesses were victims of unauthorized transactions against their business account, with losses totaling $3.1Billion. Javelin LLC

Business identity theft caused $268 million in damage in 2016, up from $122 million in 2015. IRS

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Trade –(AR & AP)Bust outBusiness Id TheftFinancial StatementPayment / Trade-RingBusiness Credit Coach

MortgageHealthcare –MedicalEmbezzlementTax EvasionMoney LaunderingPonziWire FraudMail FraudBriberyForgeryConspiracyShell / Shelf CorpPublic Corruption

ACFE

So what kind of business fraud are we talking about?BUSINESS-TO-HIGH RISK & FRAUD

CyberCryptoeconometricsConvergence (e.g. Iot and Crypto)Unexplainable AIFake newsDark WebSilent Data Breach

EMERGING

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Understanding traditional types of malfeasance does not guarantee understanding of new behaviors

Two Men Charged With $900,000 Interstate Theft SchemeNEWARK, N.J. - Two New Jersey men were arrested today for their roles in a scheme to fraudulently obtain more than $900,000 in commercial and residential merchandise from various companies, Acting U.S. Attorney William E. Fitzpatrick announced. Roy Depack, a/k/a “Ray Depack,” a/k/a “Roy Soriano,” a/k/a “John Soriano,” 42, of Elizabeth, New Jersey, and Louis J. Pobutkiewicz Sr., 39, of Newark, are charged by complaint with conspiracy to commit mail and wire fraud. Single Family House, Apartments

Retail Stores, Restaurants

Business Identity TheftPlace Orders as an agent Representing

Impersonated Business - Identity Theft Victim VICTIM COMPANIESVarious Delivery Points Impersonating Large

Commercial Business Entities

ETC.

100+ Different Phone Numbers

Walk-In Freezer

Television

Computers

Microwave

Plasma Cutters

Commercial Ice Maker

Digital Scales

Power Tools

Bot/Automated attacksSmarter human attacksData BreachIP theftAdversarial interventions

Geo/Device IdentificationBehavior/Activity analysisAdvanced anomaly detectionControl vs. Audit/TrustFederation of AI

The future of credit management is in part up to us to write…

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C U R I O U S T I M E SA R E F L E C T I O N O N W H A T I S H A P P E N I N G I N T H E W O R L D O F A L L T H I N G S D A T A A N D A I T O D AY

M A K I N G O L D D E C I S I O N S I N N E W WAY SH O W W E A R E A B L E T O U S E M O D E R N D A T A A N D T E C H N O L O G Y T O A N S W E R N E W Q U E S T I O N S I N T H E C O N T E X T O F B U S I N E S S D E C I S I O N S

L O O K I N G A H E A DA L O O K A T S O M E O F T H E F U T U R E D I S R U P T I O N S I N T E C H N O L O G Y A N D W O R K F O R C E A N D S O M E A D V I C E O N H O W T O G E T R E A D Y

TODAY

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With machine learning anything is possible.That is sometimes part of the problem…

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The definitions, objectives and methods of “Artificial Intelligence” continue to evolve

Architecture of the “projectome” Network model of ‘target’ and ‘source’ nodes via anatomical tracing

…explicitly model the human brain

……or loosely approximate the mental models

Multistate Markovian Survival Model

Bayes’ Network of perfume characteristics

• Mimic human behavior

• Mimic thinking

• Project human intent

• Advise humans

• Behave intelligently

• Behave rationally

• Behave empathetically 

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

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 Artificial Intelligence

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Thinking about relationships

Public data Cloud

Customer 1 Cloud

Customer 2 Cloud

Partner 1 Cloud

Partner 2 Cloud

Ink Blot, City 1

DUNS D1.1, D1.2, D1.3,…D1.n

Ink Blot, City 2DUNS D1.1, D1.2, D1.3,…D1.n

Some examples

• Supply Chain / Integrated Value Chain

• Understanding Signals derived from changes to business information

• Discovering and investigating clusters of unusual behavior

• Exploring the impact of new regulation (e.g. privacy)

• Understanding intra‐regional opportunities (e.g. cross‐border)

• Exploring the impact of new market forces (e.g. Brexit)

• Studying the real or potential impact of supply chain interruptions (e.g. disasters)

• Investigating emerging capabilities (e.g. reputational risk)

• Malfeasance

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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

Black Cat problems…

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The use of: data science tools, machine learning/artificial intelligence are keys our success to mitigate B2B malfeasance

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C U R I O U S T I M E SA R E F L E C T I O N O N W H A T I S H A P P E N I N G I N T H E W O R L D O F A L L T H I N G S D A T A A N D A I T O D AY

M A K I N G O L D D E C I S I O N S I N N E W WAY SH O W W E A R E A B L E T O U S E M O D E R N D A T A A N D T E C H N O L O G Y T O A N S W E R N E W Q U E S T I O N S I N T H E C O N T E X T O F B U S I N E S S D E C I S I O N S

L O O K I N G A H E A DA L O O K A T S O M E O F T H E F U T U R E D I S R U P T I O N S I N T E C H N O L O G Y A N D W O R K F O R C E A N D S O M E A D V I C E O N H O W T O G E T R E A D Y

TODAY

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”

Source: https://www.forbes.com/sites/gilpress/2017/01/23/top‐10‐hot‐artificial‐intelligence‐ai‐technologies/#10eb1f7c1928

The state of all things AI also continues to evolve

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Truth be told, it’s overwhelming and more than a bit unstable…It will never be simpler.

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We must continue to challenge our perspectives and our beliefsNew Behaviors Changing Perspectives Complex Issues

New Opportunity

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And what about the next generation?

In the past, we looked for…

• Data Curator• Analyst• Modeler• Statistician• Methodologist

Now, we need all of that and more!

• Coder• Governance Expert• Problem Formulator• Detective• Visionary• Story‐Teller• Diplomat

Some Thoughts on The Future

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New questions to ask…What do we have to believe?(Stability, permissibility)

What happens if we do nothing?           (New malfeasance, eroding relevance)

Who are the disruptors?

What do we know?How do we know it (explainability)?What has to remain true (reference frames)?

What is possible if we work together?

“While technology is important, it's what we do with it that truly matters.”

Muhammad Yunus, economist, Nobel Prize winner

Thank you Anthony J. Scriffignano, Ph.D.SVP/Chief Data ScientistDun & Bradstreet@Scriffignano1

Presentation Title:Artificial Intelligence Will Continue to Transform Modern Credit Management: Can We Handle It?

Presentation Abstract:Credit decisions have always been informed by data. Data about the counterparties in a relationship, the nature of a transition and historical risk are but a few examples. We are now operating in an era where virtually all of the underlying data is changing in curious and sometime alarming ways. Artificial intelligence (AI), a huge buzzword right now, is one of the driving disruptive forces. While AI is not a new notion, it has taken on increased focus with the explosion of Big Data over the past few years. Machine learning holds the potential to make AI even more effective in the commercial credit industry, and may be a catalyst for AI’s greater adoption. However, some very big questions arise regarding our ability to explain methods and to comply with regulations. As AI increases in complexity, we need to examine the pivotal role that data and the inferences drawn from it by our digital agents have in determining the success businesses will have—and some pitfalls to watch out for as organizations adopt these new technologies.

This session, both relevant and occasionally irreverent, will cover three main themes:Curious Times: An reflection on what is happening in the world of all things data and AI todayMaking Old Decision in New Ways: How we are able to use modern data and technology to answer new questions in the context of business decisionsLooking Ahead: A look at some of the future disruptions in technology and workforce and some advice on how to get ready