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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
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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
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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
• 513-900-4637
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