CYBERSOURCE FRAUD MANAGEMENT e-commerce Day Lima, Peru August 31, 2010 Kathy Reeves Business Development Manager kreeves@cybersource.com
Fraud Management Process Automated Screening Orders Detectors Rules Reject Chargeback Management Manual Review Tuning & Analytics
Automated Screening Orders Detectors Rules Reject 50% say Fraud is cleaner # Detection Tools = 7 Manual Review Tuning & Analytics Chargeback Management
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% 2008 2009 0,7% 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
% 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
Automated Screening Orders Detectors Rules Review Rate 15-46% Reject # Systems/Data Input Interfaces 12 Chargeback Management Tuning & Analytics Source: 2010 CyberSource Fraud Report
Automated Screening Orders Detectors Rules 2-6% Reject Chargeback Management Tuning & Analytics Source: 2010 CyberSource Fraud Report
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?
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
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
Automated Screening Orders Detectors Rules Reject Chargeback Management Manual Review Tuning & Analytics
Detectors & Rule Console Built-in Detectors & Data Sources DATA QUALITY And works with
Order & Data Sources 1 2 Correlation Engine & Modeling Order Data Individual Test Results Statistical Anomalies 3 Your Business Rules 4 Accept Review Reject
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
Example: Correlation Engine & Multi-Merchant Model Your Order Mary Smith 4XXXXXX0453 mary@gmail.com D-Fingerprint: ABC Emirates Tricia Lim 4XXXXXXXX0453 spirit@yahoo.co.uk D-Fingerprint: XYZ Air Canada TAM Tricia Lim 4XXXXXXXX0123 woti@aol.com D-Fingerprint: ABC UK Retailer Results Name changes: Multiple Credit cards: Multiple Email changes: Multiple Devices: Multiple Output Example Score 0-99 Imran Cochin 5XXXXXXXX7395 saab@hotmail.in D-Fingerprint: ABC US Retailer Pablo Jimenez 4XXXXXXXX6329 pablo@yahoo.com D-Fingerprint: XYZ Global, Multi-Merchant Air France Intelligence Tricia Lim Adam Jones 4XXXXXXXX0453 saab@hotmail.in D-Fingerprint: XYZ 4XXXXXXXX6329 devil@gmail.com 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)
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
Automated Screening Orders Detectors Rules Reject Chargeback Management Manual Review Tuning & Analytics
Case Management with One-Click Validation +
Google Maps: Validate shipping or billing address
Automated Screening Orders Detectors Rules Reject Chargeback Management Manual Review Tuning & Analytics
Reporting and Analytics Performance Reports: Screening Profiles Rules Review Process
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
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
The Most Widely Used Online Fraud Management Solution, Worldwide
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
CYBERSOURCE FRAUD MANAGEMENT Thank you!! e-commerce Day Lima, Peru August 31, 2010 Kathy Reeves Business Development Manager kreeves@cybersource.com