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Analytics as a Service: Fraud Case Study for Financial Institutions

Stu Bradley, Senior Business Director of Security Intelligence Practice, SAS

David Mattei, Vice President, Financial Institutions Product Management, Vantiv

Analytics as a ServiceFraud Case Study for

Financial Institutions

April 2015

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Vantiv

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Simplifying Payments Innovation for

Merchants and Financial InstitutionsOmni-Channel Commerce

eCommerce mPOS POS

eCommerce, Direct Commerce, Government, QSR, Retail,

Restaurant, Supermarket, University, Utility

Mobile

Payment Processing Strength

40YEARS

PaymentsInnovation

4

$760Volume

Processed

B I L L I O N

20.1Transactions

B I L L I O N

Merchants & Financial Institutions

5

1400Financial

Institutions

+500Merchants

T H O U S A N D

+ #2US Merchant

Acquirer

The Good Ol’ Days

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Current Reality

US Card Fraud Losses

7

5.6B6.0B

6.7B7.5B 7.7B

8.6B9.1B

Source: Aite, Payment Card Fraud Management report, Apr 2015

Losses shared across merchants and financial institutions

FI Fraud Management Challenges

• Fraud is a business

› Cyber crime, multi-nationals, vertical specialties

• Breaches prevalent concern

› Home Depot, Anthem, others

• FI staffing spread thinner

• FI mitigation skills falling behind fraud attack skills

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Is Fraud as a Service Viable?

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Vantiv vs. FI

Performance Management

Gross fraud (bp) 40%

Cardholder experience at POS 56%

Average loss / card 24%, $76/card

Average number fraudulent

transactions / card9%, 0.25 trans/card

RT False Positive Ratio 3:1

Vantiv’s Legacy Fraud Solution

People Process Technology

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Technology Limitations

Technology Gap

Where we were

• FaaS not scalable

• People and Process

intensive

• Analytics not at acceptable

levels

• Multiple systems

Desired state

• Scalable solution

• Single, integrated system

• Strong, predictive analytics

• Allow People to go broad

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Market Evaluation

• RFP

• Extensive due diligence

among 2 finalists

• SAS Enterprise Fraud

Manager selected

12Source: Forrester Research, Feb 2013

SAS Hybrid Neural Model

• Custom model for Vantiv’s portfolio of FIs

• Augmented by consortium data from other SAS

customers

• Best of both worlds

› Better than a custom-only model

› Better than a consortium-only model

• 14 months of auth and fraud data to build model

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Fraudulent Card Detection

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0%

10%

20%

30%

40%

50%

60%

70%

1:1 3:1 5:1 7:1 9:1 11:1 13:1 15:1 17:1 19:1

SAS Model

Legacy Model

Case Detection Rate

False Positive Ratio

Legacy Model SAS Model % Improvement

CDR* 42.2% 65.4% 55%

Fraudulent Dollar Detection

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Legacy Model SAS Model % Improvement

VDR* 41.3% 75.6% 83%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

1:1 3:1 5:1 7:1 9:1 11:1 13:1 15:1 17:1 19:1

False Positive Ratio

Value Detection Rate

SAS Model

Legacy Model

Vantiv’s Fraud Paradigm Shift

Legacy Solution Current Solution

Rules-based Analytics-based

350,000 rules < 1,500 rules

Rules detected 70% of fraud Analytics detect 70% of fraud

FaaS limited to < 200 FIs FaaS support for 1,400+ FIs

Shared fraud management between FIs and Vantiv

Fully outsourced solution offered by Vantiv

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Paradigm shift enabled by analytics

FI Benefits

• Fraud detection speed

› 32% improvement

• Loss per card

› 28% improvement

• Overall fraud reduction

› $16MM across all

financial institutions

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1.5

2.2

-

0.5

1.0

1.5

2.0

2.5

Current Solution Legacy Solution

Fraudulent Trans/Card

$133

$184

$0

$50

$100

$150

$200

Current Solution Legacy Solution

Average Loss/Card

SAS / Vantiv Partnership

• Strong partnership

• SAS monitors neural model performance

• Vantiv / SAS collaborate on model refreshes

• SAS has a vested interest in Vantiv’s success

• Simplified Vantiv’s ability to conduct business

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Contact

• David Mattei

• VP, Product Portfolio Manager

• Vantiv

• David.Mattei@vantiv.com

• 513-900-4637

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