CYBERSOURCE FRAUD MANAGEMENT

Transcription

1 CYBERSOURCE FRAUD MANAGEMENT e-commerce Day Bogota, Colombia December 1, 2010 Daryl Williams Manager, Sales Engineer Kathy Reeves Business Development Manager

2 Total market size based on JP Morgan forecast; country growth rates based on CyberSource analysis

3 International Order Acceptance in 2009 % of Merchants Accepting International Orders From % of Merch hants 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 81% 72% 71% 68% Over half of merchants accept online orders from outside the U.S. and/or Canada** In 2009 these orders represented on average 21% of total orders up from 17% in % 64% 63% 62% 53% United Australia Germany France Italy Mexico Spain Japan Hong Kingdom Kong 52% 51% 51% Average # of Countries per Merchant 9 48% Singapore Brazil China South Korea 48% Taiwan 47% India n=191 Note: A list of countries was provided, but merchants were also allowed to add any country that was missing from the list. (The list of countries provided changed in 2008.) Base: Merchants accepting international orders Results < 25% not shown Q4b. From which of the following countries, outside the U.S. and Canada, do you accept online orders? Please select all that apply. **Note: 54% in 2009; 52% in 2008, 59% in

4 Fraud Management Process Automated Screening Orders Detectors Rules Reject Chargeback Management Manual Review Tuning & Analytics

5 Fraud Rates in U.S./Canada (Overall and by Online Segment) International Fraud Rate 2% Overall Digital Goods/ Svcs Media & Entertainment Apparel/ Jewelry Health Consumer Electronics Household & General Merchandise Education/ Government Source: 2010 CyberSource Fraud Report

6 Top Priority Strategy / Area of Focus % Manual Review (tasks / workflow) 60% Automated Detection 20% Process Analytics 2% Outsourcing 2% Other Source: 2010 CyberSource Fraud Report

7 Automated Screening Orders Detectors Rules Reject # Detection Tools = 7 Chargeback Managementg Manual Review Tuning & Analytics 50% say Fraud is cleaner

8 Automated Fraud Detection Tool Use Fraud Detection Tool Usage 2009 Merchants $25M+ Online Revenue Validation Services CVN (Card Verification Number) Address Verification Service Postal address validation services Verified by Visa/MasterCard SecureCode Telephone # verification/reverse lookup Paid for public records services Credit history check Out-of-wallet or in-wallet challenge/response Customer order history Negative lists (in-house lists) Order velocity monitoring Fraud scoring model-company specific Positive lists Customer website behavior analysis IP geolocation information Device "fingerprinting" Shared negative lists-shared hotlists Multi-merchant purchase velocity Other 80% 9% 86% 3% % Currently Using 35% 14% 16% 12% % Planning to Implement 33% 24% 12% 17% 4% 5% 10% 5% Your Proprietary Data/Customer History 61% 10% 75% 5% 66% 12% 53% 17% 41% 14% 19% 19% Purchase Device Tracing 52% 26% 18% 45% Multi-Merchant Data/Purchase History 23% 19% 19% 12% 6% 9%

9 No Silver Bullet Paid for public records services Contact customer to verify order Credit history check Verified by Visa/MasterCard SecureCode Address Verification Service CVN (Card Verification Number) Telephone # verification/reverse lookup Out-of-wallet or in-wallet challenge/response Postal address validation services Contact card issuer/amex CVP % Merchants Using Tool that Selected it as One Of Their Top Three Most Effective 2009 Validation Services 2% 10% 9% 20% 19% 16% 16% 15% Your Proprietary Data/Customer History Fraud scoring model-company specific Negative lists (in-house lists) Customer website behavior analysis Customer order history Order velocity monitoring 14% Positive lists 7% Purchase Device Tracing IP geolocation information Device "fingerprinting" Multi-Merchant Data/Purchase History Multi-merchant purchase velocity Shared negative lists-shared hotlists 11% 16% 22% 22% 21% 26% 32% 31% 37% 36% Base: Merchants with annual online sales $25M who use tool : automated or manual (excludes None) Q10c. Of the tools your company currently uses to help detect online payment fraud or assess fraud risk for online orders, please select the most effective. Please select up to three. *Caution: small base

10 Simplifying Payment Management Business Improvements Protect Keep more revenue Keep brand safe Optimize Operate with less complexity/cost Access better analytics to manage Grow Reach more customers, faster Change/add without disruption 10

11 Risk Analysis Screening Rules UI Case Management UI Reporting & Analytics UI

12 Automated Screening Orders Detectors Rules Reject Chargeback Management Manual Review Tuning & Analytics

13 Payment Types Credit & Debit Cards Gift & Pre-Paid Cards echecks & Direct Debits PayPal & BML Sales Channels Website Call Center / IVR Batch Point Of Sale Technology Partners: DATA QUALITY

14 Data Correlation Provides Fraud Intelligence 15 years experience Billions of transactions modelled Over 200 tests applied to every transaction Output Example Score 0-99 Factor Codes F (Fraud List) (> 20) G (Geolocation inconsistency) N (Nonsensical input) Info Codes (>125) MM-BIN UNV-ADDR VEL-NAME (BIN mismatch) (unverifiable address) (multiple names with card) Increasing insight No black box Merchants marking suspicious transactions Reviewer decisions Chargeback automarking by banks Partnership with Visa

15 Identity Morphing Detection Home Depot TAM Your Order Mary Smith 4XXXXXX0453 D-Fingerprint: ABC Results Name changes: Multiple Credit cards: Multiple changes: Multiple Devices: Multiple Tricia Lim 4XXXXXXXX0453 D-Fingerprint: XYZ Air Canada Imran Cochin 5XXXXXXXX7395 D-Fingerprint: ABC Nike Adam Jones 4XXXXXXXX0453 D-Fingerprint: XYZ Tricia Lim 4XXXXXXXX0123 D-Fingerprint: ABC Timberland Global, multi-merchant intelligence Pablo Jimenez 4XXXXXXXX6329 D-Fingerprint: XYZ Pacific Sunwear Tricia Lim 4XXXXXXXX6329 D-Fingerprint: QRS

16 Automated Screening Orders Detectors Rules Reject Chargeback Management Manual Review Tuning & Analytics

17 Case Management with One-Click Validation +

18 Automated Screening Orders Detectors Rules Reject Chargeback Management Manual Review Tuning & Analytics

19 Reporting and Analytics Performance Reports on: Screening Profile Rules Review Process

20 Fraud Screen Flow Using CyberSource Order 4D Validation Business Rules - Flexible User Console Active Passive Accept/Reject Decision Performance Management Strategy Design Process Optimization Rule Tuning Reviewer Performance Review Case Management Reporting Analytics Insight

21 The Most Widely Used Online Fraud Management Solution, Worldwide

22 Airline Partners

23 Fraud Management Expertise Thousands of merchants globally Since 1995 Global, multi-merchant view of fraud trends Secure, reliable, trusted public company Active industry leadership Board Member: Merchant Risk Council - USA Board Member: Merchant Risk Council - EU Member: PCI Security Standards Council Trusted advisor Trainer (US): NSA, CIA, FBI Advisor (UK): Shadow Home Affairs Minister Annual fraud report + airline fraud report Long-standing Visa partnership on fraud

24 Global Presence Asia: 2000 CyberSource K.K. established 2000 JV with Trans-Cosmos, Inc. Sales, Marketing, Support, Operations Datacenter: Tokyo Europe: 1997 HQ: UK Sales, Marketing, Support, Operations Datacenter: London Engineering: Belfast, Northern Ireland USA: 1997 HQ: Mountain View, CA Offices throughout US Engineering, Operations, Sales, Marketing, Admin Datacenters: Arizona, California, Colorado, Washington Acquired by Visa: July 2010

25 Gracias! Daryl Williams Kathy Reeves