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© 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac.

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Presentation on theme: "© 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac."— Presentation transcript:

1 © 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent. More than a Numbers Game Building Better Strategies that Leverage Unique Kinds of Data Dennis D’Ambrosio Senior Fraud Operations Analyst KeyBank Michael Holloway Senior Fraud Operations Analyst KeyBank Lamar Shahbazian Sr. Director FICO

2 © 2014 Fair Isaac Corporation. Confidential. Congratulations! Bank: Hello, this is your bank. The check you deposited is counterfeit and your account is now overdrawn $2,400. It can’t be, I won the Clearing house sweepstake. 2 Image Source:

3 © 2014 Fair Isaac Corporation. Confidential. Despite declining check usage, check fraud is still ever present Check Fraud The 2013 Federal Reserve Payment Study. *RDIs include NSFs, closed accounts, refer to maker, stop payments, uncollected funds, remotely created checks, no account found, government reclamations, and other return loss reasons. Trend in Noncash Payments Source of Check Fraud Losses Percentage of Total Losses % of Total Losses 1Q061Q071Q081Q091Q101Q111Q121Q CounterfeitsForgeries 3

4 © 2014 Fair Isaac Corporation. Confidential. Check Printing Past and Present PastPresent ► MICR Ink required ► Limited check printing resources ► Print anywhere ► Image exchange networks ► Mobile Deposit 4

5 © 2014 Fair Isaac Corporation. Confidential. Check Detection Past and Present ► Past ► Feel the paper ► Look for the perforation ► Look for discoloration ► Hold it up to the light ► Present Challenges… ► No Paper to handle ► Black & white images 5

6 © 2014 Fair Isaac Corporation. Confidential. ► Image Fraud Detection ► Takes check fraud detection beyond the MICR line ► Signature and check stock analysis Image Analysis Crossings Upstrokes Curves and Loops Enclosed Areas Signature characteristics are extracted, weighed and compared using a neural network and fuzzy logic. MICR Data Payee Address General design 6

7 © 2014 Fair Isaac Corporation. Confidential. ► Signatures and check stocks are digitized and scored ► ASV Result Codes = analysis outcomes: Signature OK, different, new, etc ► ASV Match Rates: 0–100 confidence score ► APIA Result Codes = analysis outcomes: OK, validation failed, new, etc ► APIA Match Rates: 0–100 confidence score Data Elements 7 ASV RESULTASV MATCHRATEAPIA RESULTAPIA MATCHRATE

8 © 2014 Fair Isaac Corporation. Confidential. Why Use Decision Trees? ► Run large volumes of data and “find the nuggets” ► Implement existing rules ► Profiling fraud against non-fraud allowed us to look for improvement ► Visual ► Easy to explain Strategy Design 8

9 © 2014 Fair Isaac Corporation. Confidential. Decision Trees Help Verify and Suggest Potential Improvements ► Image analysis output = result codes and match rate scores ► Rules drive alerts based on scores, account type, transaction data ► Strategy Trees validate rules and performance Strategy Design 9

10 © 2014 Fair Isaac Corporation. Confidential. ► System Upgrade—new analysis engine and risk indicators ► New Strategy Design What’s Next? 10

11 © 2014 Fair Isaac Corporation. Confidential. Wire Fraud 11

12 © 2014 Fair Isaac Corporation. Confidential. Wire Fraud “Wire Fraud is difficult to detect; comes in many different colors and flavors.” David Pollino Senior Vice Presitdent Enterprise Fraud Prevention Officer at Bank of the West 12

13 © 2014 Fair Isaac Corporation. Confidential. ► KeyBank implemented enterprise fraud detection application 2011 ► Moved from static rules to behavioral based application ► Over-compensated by implementing additional rules ► And the result was… Wire Transfer Fraud Detection 13 Image from:

14 © 2014 Fair Isaac Corporation. Confidential. ► White-boarded rules to eliminate unnecessary alerts from Q ► Improvement but monthly volume was still too high Goal: Continue to reduce alerts while maintaining frauds identified Detection and Volume Challenges White-boarded rules 14 Alerts Frauds

15 © 2014 Fair Isaac Corporation. Confidential. Less than 50 Frauds out of 960,000 Transactions ► Decided to pursue more extensive rule analysis using decision trees ► Built a transaction file of frauds and non-frauds ► Initially met via webex, then opted to have two day “bootcamp” ► Interactively dissected ‘pockets’ of fraud ► Constructed several rule-set options in a few hours ► Learning: Risks unique to specific channels became evident Looking for Needle in a Stack of Haystacks 15

16 © 2014 Fair Isaac Corporation. Confidential. ► Implemented new rules in Q3 and Q ► Reduced alerts created by 39% Q3 over Q2, further reduced alerts by 45% Q4 over Q ► Fine tuning in 2014 has continued to reduce alerts and increase detection rates Balancing Volume with Quality of Alerts Frauds Alerts 16

17 © 2014 Fair Isaac Corporation. Confidential. While alerts went down, averted losses went up ► New rules led to richer alerts for operations team ► Majority of all frauds were detected ► Ongoing analysis for continued optimization Stellar Results 17 Alerts Averted

18 © 2014 Fair Isaac Corporation. Confidential. Next Steps 18 © 2014 Fair Isaac Corporation. Confidential. ► Continue to evaluate rule effectiveness of rules on an ongoing basis ► Most recent rule project leveraging FICO ® Analytic Modeler Decision Trees Professional software is for Mobile Deposit Fraud detection ► Card rules on-deck

19 © 2014 Fair Isaac Corporation. Confidential. ► FICO’s analytic tools have transformed our fraud detection methodology; we use data to drive insights and empirically based decisions ► Our operations teams have a greater confidence in alerts; our rules look at several factors such as customer segment, channel, business logic in addition to score ► This approach has yielded improved false positive rates and a reduction in fraud losses Conclusion 19

20 © 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent. Thank You! Dennis D’Ambrosio Michael Holloway Lamar Shahbazian

21 © 2014 Fair Isaac Corporation. Confidential. Learn More at FICO World 21 Related Sessions ► Product Showcase: Analytic Modeling in the Cloud Products in Solution Center ► FICO ® Analytic Modeler Decision Tree Professional Experts at FICO World ► Lamar Shahbazian ► Jeff Dandridge Blogs ►

22 © 2014 Fair Isaac Corporation. Confidential. Please rate this session online! 22 Dennis D’Ambrosio Michael Holloway Lamar Shahbazian

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