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 Service
Fraud Case Study for
Financial Institutions
April 2015
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Vantiv
Simplifying Payments Innovation for
Merchants and Financial Institutions
Omni-Channel Commerce
eCommerce
Mobile
mPOS
POS
eCommerce, Direct Commerce, Government, QSR, Retail,
Restaurant, Supermarket, University, Utility
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Payment Processing Strength
40
YEARS
Payments
Innovation
20.1 $760
B I L L I O N
BILLION
Transactions
Volume
Processed
4
Merchants & Financial Institutions
500
THOUSAND
Merchants
+
#2 1400 +
US Merchant
Acquirer
Financial
Institutions
5
The Good Ol’ Days
Current Reality
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US Card Fraud Losses
8.6B
7.5B
5.6B
6.0B
9.1B
7.7B
6.7B
Losses shared across merchants and financial institutions
Source: Aite, Payment Card Fraud Management report, Apr 2015
7
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?
Vantiv vs. FI
Performance Management
Gross fraud (bp)
40%
Cardholder experience at POS
56%
Average loss / card
24%, $76/card
Average number fraudulent
transactions / card
9%, 0.25 trans/card
RT False Positive Ratio
3:1
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Vantiv’s Legacy Fraud Solution
People
Process
Technology
Technology Limitations
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Technology Gap
Where we were
Desired state
• FaaS not scalable
• People and Process
intensive
• Analytics not at acceptable
levels
• Multiple systems
•
•
•
•
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
Source: Forrester Research, Feb 2013
12
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
CDR*
Legacy Model
SAS Model
% Improvement
42.2%
65.4%
55%
Case Detection Rate
70%
60%
SAS Model
50%
40%
Legacy Model
30%
20%
10%
0%
1:1
3:1
5:1
7:1
9:1
11:1
13:1
False Positive Ratio
15:1
17:1
19:1
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Fraudulent Dollar Detection
Legacy Model
SAS Model
% Improvement
41.3%
75.6%
83%
VDR*
Value Detection Rate
90%
80%
70%
SAS Model
60%
50%
40%
Legacy Model
30%
20%
10%
0%
1:1
3:1
5:1
7:1
9:1
11:1
False Positive Ratio
13:1
15:1
17:1
19:1
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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
Paradigm shift enabled by analytics
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Fraudulent Trans/Card
FI Benefits
2.5
1.5
• Fraud detection speed
› 32% improvement
1.0
Current Solution
› 28% improvement
› $16MM across all
financial institutions
1.5
0.5
• Loss per card
• Overall fraud reduction
2.2
2.0
Legacy Solution
Average Loss/Card
$200
$184
$150
$100
$133
$50
$0
Current Solution
Legacy Solution
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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
[email protected]
513-900-4637
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