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May 2017 Scott Bright, VP Fraud Product Strategy Fraud Machine Learning A digital arms race that holds security in the balance

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Page 1: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

May 2017Scott Bright, VP Fraud Product Strategy

Fraud Machine LearningA digital arms race that holds security in the balance

Page 2: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

Session Agenda

2

• What is Artificial Intelligence Machine Learning?

• Technology used by criminals

• Why does the payment industry care?

• Strategy and tools in the market to combat fraud

Page 3: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

3

What is Artificial Intelligence

Machine Learning?

Page 4: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

Machine LearningEvolution of Machine Learning Approaches

• Statistical algorithms use mathematical probability to predict outcomes

• Classical statistics-based solutions can’t improve performance over time

• Cognitive processes model human thinking to solve problems

– Heuristic rules

– Inductive logic

Cognitive models can improve if new rules are added manually

• Machine learning is a class of computer algorithms that improve their performance by learning from positive examples.

– The algorithm is trained with specific examples with known solutions

– The algorithm then predicts answers from unknown datasets

Page 5: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

Machine Learning Algorithms:

Classification Techniques

Clustering is the task of grouping a set of objects in such a

way that objects in the same group (called a cluster) are more

similar to each other

Page 6: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

AI Neural Networks Approach:

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Neural Networks are a family of models inspired by biological neural networks (the central nervous

systems of animals, in particular the brain) and are used to estimate or approximate functions that can

depend on a large number of inputs and are generally unknown.

Benefits

• Highly abstract data

representations

• Adaptive, rapidly

improving

performance

• No need for

interpretability

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7

Technology used by

criminals

Page 8: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

Available 24/7/365 to anyone

The Big Business of FraudFraud as a Service

Who is involved:

• Organized crime syndicates

• Foreign governments

• U.S. Domestic crime rings

• Individuals accessing the Dark Web

Page 9: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

How the Dark Side Runs Their Business

• Criminal enterprises with corporate structures:

– Profit/loss margins

– Criminal business strategies

– Targeted objectives

– Leveraged with other enterprises

• Criminal factions don’t want just the card:

– Database-driven

– Cardholder demographics

– Exploit data for maximum penetration

– Use malware to steal data where appropriate

• Use and take advantage of consumer spending patterns:

– Fraud trends mirror everyday spend categories.

– “Shock and awe” fraud is occasional rather than the norm.

– Use high-profile goods to convert to a cash equivalency (i.e., gas, gift cards,

cigarettes, etc.)

Page 10: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

10

Why does the payment

industry care?

Page 11: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

Data breaches on the rise

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• Data Breaches increased by 40% in 2016

• All time record high with 15.4 million victims

affected

• Business sector topped the list with 45.2%

overall

• Healthcare 2nd with 377 reported breaches or

34.5%

http://www.prnewswire.com/news-releases/data-breaches-increase-40-percent-in-2016-finds-new-report-from-identity-theft-resource-center-and-cyberscout-

300393208.html

Industry Sectors Percentage of Overall Breaches

Page 12: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

Cybercriminals have better toys

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• Shimmer devices used on ATM and card

readers

• Fraudulent online purchases made in groups

of small purchases to go undetected

• New technology created for fraudsters that

span across all business markets that never

used fraud mitigation tools

Source: http://www.pymnts.com/news/security-and-risk/2017/hacker-tracker-shimmers-identity-fraud-and-hacked-hotels/

Source: White Ops

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Fraud losses hit right in the bottom…line

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• By 2020, card fraud worldwide is

expected to total $31.67 Billion

• Fraud losses are expected to grow

annually 12% in 2017 & 2018

• Account takeover losses in 2016

reached $2.3B a 61% increase from

last year

Data sources from: Neustar, The Nilson Report, ThreatMetrix

Page 14: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

14

Centralize Fraud

Management

Page 15: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

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New Account Fraud Account Takeover Payments Fraud

Authentication Data AnalysisPost TransactionConsumer

Engagement

Transaction

Monitoring

ID Verification

Biometrics

OFAC

Device Authentication

Consumer ControlsTransaction Monitoring

Machine Learning

Business Intelligence

Predictive AnalyticsTransaction WarrantyCompromised Cards

Dedicated Analyst

Custom Rules

Global Rules

2-way Messaging

Fraud Mitigation

AssociationsTravel

Notification

Attack Types

Solutions in the Market

CAMS Alerts

Mitigation Categories

Malware Detection

Internal Fraud

Employee Fraud

Detection

Automated Chargeback

Dynamic Jailbreak Root Detection

ANI Spoofing

Fraud Solutions LandscapeAs the complexity of payments increases so does the demand for new fraud solutions

Page 16: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

Enterprise Strategy for Omni-Channel Protection

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Leverage each channel’s unique processing to create a holistic view to fraud mitigation

• Fraud strategy across

multiple product lines

including credit, debit,

prepaid, & merchant

• Create a forum to allow

various fraud analyst

groups to talk and share

• Identify gaps in business

lines to start the solution

process

• Combine various fraud

roadmaps to drive into a

single through line

Page 17: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

Fraud in Payments Breakout SessionsAdditional sessions to attend related to payments fraud mitigation

Application Fraud: The reemergence of an old tactic with a

new approach

Wednesday 2:00 – 3:00pm & 3:30-4:30pm

Room: Tampa 2

The Looming Account Takeover Threat

Tuesday 1:45 – 2:45pm

Room: Sun 4

Fraud Analytics and how to Harness the data

Tuesday 3:15 – 4:15pm

Room: Tampa 2

Consumer engagement: A Cardholder’s New Role to Stop

Fraud and Save You Money

Thursday 9:45 – 10:45am

Room: Tampa 2

Fraud Machine Learning: A Digital Arms Race that Holds

security in the balance

Tuesday 3:15 – 4:15pm

Room: Sun 6

Kiosk 308AStop by to learn more in

the Expo Hall

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18

One last thought…

“If you don't like change, you're going to like

irrelevance even less.”

-- General Eric Shinseki, Chief of Staff, U.S. Army

The fraud movement continues to morph into an industry with untraditional competitors

changing the traditional business models and technology

Together, with FIS we can lead the payments market so regardless of size – can

effectively compete against fraud

Create a strategy through leadership of defining and leading sustainable change within

how fraud is mitigated

This focus on fraud is critical for our success and the industry

We will not have a second chance, the time is now

Page 19: Fraud Machine Learning - FIS Globalempower1.fisglobal.com/rs/650-KGE-239/images/1228_Fraud Machine... · The Big Business of Fraud Fraud as a Service Who is involved: ... Fraud Analytics

Thank youScott Bright

[email protected]

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©2017 FIS and/or its subsidiaries. All Rights Reserved. FIS confidential and proprietary information.