CYBERSOURCE FRAUD MANAGEMENT

Transcription

1 CYBERSOURCE FRAUD MANAGEMENT e-commerce Day Lima, Peru August 31, 2010 Kathy Reeves Business Development Manager

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

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

4 Fraud Rates in U.S./Canada (Overall and by Online Segment) 3,0% 2,5% 2,0% 1,5% 1,0% 0,5% 0,0% 1,1% 1,0% 1,0% 1,0% 0,9% 0,9% 1,3% 1,1% 0,9% 0,9% 2,0% 1,5% 1,4% 0,6% ,7% 0,6% 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

5 % of Revenue Lost to Payment Fraud (2009 Airline Online Fraud Report) Exp Region Airline Type 3,8% 2,6% 1,3% 1,1% 1,1% 1,2% 1,9% 1,6% 1,2% 1,1% 0,6% 0,6% n=61 *Caution: small base ** Online Revenues $100 + Million

6 Automated Screening Orders Detectors Rules Review Rate 15-46% Reject # Systems/Data Input Interfaces 12 Chargeback Management Tuning & Analytics Source: 2010 CyberSource Fraud Report

7 Automated Screening Orders Detectors Rules 2-6% Reject Chargeback Management Tuning & Analytics Source: 2010 CyberSource Fraud Report

8 3 Questions to Ask 1. How will I detect increasingly cleaner fraud? 2. How will I scale operations? [facing static budget, increasing order volume and demand for higher levels of service] Expertise Capacity Service Delivery (24x7, global) 3. Do I have systems/process to manage and optimize?

9 How Can CyberSource Help? Detect increasingly cleaner fraud (including botnets) CyberSource Decision Manager System Scale in-house team and optimize operating performance In-house team (expertise, capacity) Process management/analytics Managed Services Scale operations through outsourcing

10 Risk Analysis/Detectors Screening Rule Builder Case Management System Reporting & Analytics UI Payment Types Credit & Debit Cards Gift & Pre-Paid Cards echecks & Direct Debits PayPal & BML Sales Channels Website Call Center / IVR Batch Point Of Sale

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

12 Detectors & Rule Console Built-in Detectors & Data Sources DATA QUALITY And works with

13 Order & Data Sources 1 2 Correlation Engine & Modeling Order Data Individual Test Results Statistical Anomalies 3 Your Business Rules 4 Accept Review Reject

14 Built-in Data Correlation Provides Fraud Intelligence Correlation Engine & Modeling Order Data Individual Test Results Statistical Anomalies Correlates 200+ tests and relationships for every transaction 4D tests/data Your marking of suspicious transactions Your reviewer decisions Chargeback automarking by banks Utilizes15 years online fraud modeling experience Billions of transactions modeled Includes Visa online and offline data modeling

15 Example: Correlation Engine & Multi-Merchant Model Your Order Mary Smith 4XXXXXX0453 D-Fingerprint: ABC Emirates Tricia Lim 4XXXXXXXX0453 D-Fingerprint: XYZ Air Canada TAM Tricia Lim 4XXXXXXXX0123 D-Fingerprint: ABC UK Retailer Results Name changes: Multiple Credit cards: Multiple changes: Multiple Devices: Multiple Output Example Score 0-99 Imran Cochin 5XXXXXXXX7395 D-Fingerprint: ABC US Retailer Pablo Jimenez 4XXXXXXXX6329 D-Fingerprint: XYZ Global, Multi-Merchant Air France Intelligence Tricia Lim Adam Jones 4XXXXXXXX0453 D-Fingerprint: XYZ 4XXXXXXXX6329 D-Fingerprint: QRS Factor Codes (> 20) F G N (Fraud List) (Geolocation inconsistency) (Nonsensical input) Info Codes (>125) MM-BIN (BIN mismatch) UNV-ADDR (unverifiable address) VEL-NAME (multiple names with card)

16 Device Fingerprinting with Packet Signature Inspection IP_Attributes STATIC, BOTNET_ZOMBIE STATIC, BOTNET_ZOMBIE STATIC, BOTNET_ZOMBIE DYNAMIC, BOTNET_ZOMBIE STATIC IP Activities TCP_SCAN_FLAG, CONNECTING_TO_BOTNET, SPAM TCP_SCAN_FLAG, SPAM TCP_SCAN_FLAG, OTHER, SPAM OTHER, SPAM SPAM

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

18 Case Management with One-Click Validation +

19 Google Maps: Validate shipping or billing address

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

21 Reporting and Analytics Performance Reports: Screening Profiles Rules Review Process

22 Performance Monitoring Strategy Design Rule Results/Tuning Queue Logic/Tuning Reviewer Performance Active Monitoring Business Performance Guarantees Orders Detectors Rules Automated Screening Reject Fraud Claims Manual Review Screening Management Performance Monitoring plus Manual Review Services Branded Customer/Bank Verifications Toll-Free Phone Line for Contacts External Validation Services Tuning & Analytics

23 CyberSource Solves Your 3 Challenges 1. How will you detect increasingly cleaner fraud? 200 Built-in Detectors/Tests and Correlation Engine 2. How will you scale operations? Expertise Capacity 24x7, global service In-House Tools + Expert Backup w/slas Outsource w/slas 3. Do you have systems/process to manage and optimize? Analytics + Expert Analysis Closed-loop Process/Reporting Management

24 The Most Widely Used Online Fraud Management Solution, Worldwide

25 Expertise You Can Trust 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

26 CYBERSOURCE FRAUD MANAGEMENT Thank you!! e-commerce Day Lima, Peru August 31, 2010 Kathy Reeves Business Development Manager