Webinar Meet Your Fraud Management Challenges By Choosing The Right Fraud Management Product

1. WebinarMeet Your Fraud Management Challenges By Choosing The Right Fraud Management Product Andras Cser, Vice President, Principal Analyst May 22, 2013. Call in at 10:55 a.m. Eastern time

2. Behavior modeling and big data fuel fraud management market growth.

3. Fraud affects profitability, reputation, and compliance The bottom line Losses due to fraud Spend on fraud prevention resources Brand value and customer loyalty Regulatory compliance Know your customer. Antimoney laundering Fraud affects all verticals.

4. Five B’s of fraud management trends Big data (volume, velocity, variety, veracity, and value of data) Behavioral analytics BYOD and mobile fraud BI data from fraud data Breadth and bundling of shared networks’ information

5. Big data Velocity Volume Variety

6. How do we avoid this?

7. Big data will transform fraud management requirements Volume Typically data quantities of more than a few or 10 TB are considered big data. Velocity Fast movement of data Streamed data Not feasible to persist data (cost or speed issues) Variety Log files Social networks’ information Textual information Device-specific data and geolocation data Low-level data (sensors, etc.)

8. Behavioral analysis

9. Malware protection on endpoints Identity proofing and vetting Fraud management in the future Adaptive and risk-based authentication Enterprise fraud management Identity resolution Behavioral profiling Link analytics and info sharing Employee fraud

10. BYOD and mobile device fraud

11. Need to manage Many and confusing definitions NFC chips in mobile devices replacing (or augmenting) EMV chips in plastic credit cards CNP payment transactions are initiated from mobile devices. eWallets (Project Oscar in the UK, Google, Amazon.com, and Apple) From native mobile applications From the mobile Web or WAP sites Mobile billing SMS-/text-message-based billing

12. BI data from fraud data: a symbiotic relationship

13. Breadth of shared and consortium information

14. Evaluated vendors: product information and selection criteria Source: February 13, 2013, “The Forrester Wave™: Enterprise Fraud Management, Q1 2013” Forrester report

15. Forrester Wave™: Enterprise Fraud Management, Q1 2013 Source: February 13, 2013, “The Forrester Wave™: Enterprise Fraud Management, Q1 2013” Forrester report

16. Forrester Wave™: Enterprise Fraud Management, Q1 2013 (Cont.) Source: February 13, 2013, “The Forrester Wave™: Enterprise Fraud Management, Q1 2013” Forrester report

17. SWOT analysis — 41st Parameter Strengths Device fingerprinting SketchMatch DataSpider (implicit link analytics) Great lift curves Opportunities Multiple verticals Banking expansion Mobility Weaknesses Rules-based only No GUI for link analytics Fraud rings are special. Slow Threats Not profitable Unclear pricing structure Weak auditing

18. SWOT analysis — ACI Worldwide Strengths Card presence (debit and credit) Statistical models Case management Opportunities Hosted solutions AML and fraud integration Stepping outside the legacy banking vertical into travel or insurance Weaknesses Link analytics is only tabular. No device fingerprinting Expensive Threats Undifferentiated strategy Stuck in the middle (3,500 employees) Customer satisfaction is low. Lack of SI partners

19. SWOT analysis — Actimize Strengths AML/KYC background Great user support Great case management Great visual representation Engineering focus Opportunities Differentiated strategy: mobile, cloud, and behavioral analytics Fraud management in addition to AML KYC Weaknesses Rule outcomes are rigid. No user configuration of statistical models Integration with third-party systems Lack of device fingerprinting Threats Slow to a SaaS model — on-premises mainly No real presence in retail, insurance, and travel verticals Small customer base (40 organizations)

20. SWOT analysis — CA Strengths 3D Secure implementation Risk scoring for 3D Secure Strong statistical models Opportunities Solution delivery models (SaaS and on-premises) Arcot integration Full stack of products in IAM and fraud Weaknesses Link analytics Case management Threats No transaction monitoring No AML/KYC Small customer base Limited to FinServ

21. SWOT analysis — CyberSource Strengths High SLA Shorter implementation time than others Velocity tracking What-if analysis PSP services Many transactions scored Opportunities Many transactions scored Improve product ad hoc reporting (false-positive and false-negative rates, dashboards, etc.) Weaknesses Weaker than industry average Lorenz lift curve (detected fraud % as a function of reviewed cases %) No rule priority settings Some rules cannot be edited Weaker case management than others Threats No AML/KYC FinServ presence

22. SWOT analysis — FICO Strengths Largest install base (9,000 organizations) Homogenous offering FICO score is well known. Adaptive analytics and neural networks Excellent revenue to the number of employees ratio Longest track record in fraud management Link analytics visualization with Infoglide Software Opportunities Insurance verticals Employee fraud management Improve cloud delivery model SaaS models Big data Weaknesses Configurability of case management No own device fingerprinting solution Threats Erosion of customer base to smaller, more agile vendors Evolve from a payment fraud solution to a true, cross-channel fraud platform Other fraud vendors picking up device fingerprint vendors faster

23. SWOT analysis — FIS Strengths Memento acquisition + AML existing solution combo Self-updating statistical model Exposed model variables Flexible pricing models (license and subscription) Opportunities Improve partner ecosystem Expand into non-FI verticals Weaknesses Not real time yet across the board — only ACH and wire Poor link analytics and multiple languages support No easy way to integrate and functionality not exposed as API/web services Threats No transparency with metrics of the solution (would not disclose FPR, etc.) No significant AML/KYC

24. SWOT analysis — SAS Institute Strengths Broadest portfolio Traditionally strong in AML/KYC Link analytics Ensemble models Big data thought leadership (HPA) Automatically learning models Broad partner ecosystem Opportunities Privately held, large company is free to innovate. Fraud data to BI data High-performance computing Weaknesses Need for excessive customization in some use cases Leasing of software only — no perpetual licenses Threats Too expensive for some organizations Cloud offerings

25. Forrester predicts . . .

26. Five B’s of fraud management trends Big data (volume, velocity, variety, veracity, and value of data) Behavioral analytics BYOD and mobile fraud BI data from fraud data Breadth and bundling of shared networks’ information

27. Fraud management market trends and key requirements Increased sophistication (three-way fraud schemes and MITB/MIM attacks) requires predictive analysis capabilities. New interlinked areas of fraud (retail, airlines, and gaming) Organized crime with customer service and warranties for CC numbers Mobile device uptake for perpetration Cross-channel fraud expansion

28. Fraud management market trends and key requirements (cont.) Fraud management becoming a commodity — need of bundling of value add (PSP, etc.) Need for vendor collaboration for sharing fraud data Vendors we hear about in inquiries about fraud management in retail: 41st Parameter, Accertify, CyberSource, Kount, Retail Decisions (ReD), ThreatMetrix, iovation, and 3rd Man

29. Vendors’ future investments recurring themes Behavioral analytics Mobile device identification Link analysis Cross-channel fraud expansion New markets: Latin America and Southeast Asia Reporting and auditing and balanced fraud dashboards

30. Andras Cser +1 617.613.6365 acser@forrester.com Twitter: @acser