Post on 15-Jul-2020
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