CYBERSOURCE FRAUDMANAGEMENT

e-Commerce DayBogota, Colombia

Daryl WilliamsManager, Sales Engineer

dwilliams@Cybersource.com

Kathy ReevesBusiness Development Manager

kreeves@cybersource.com

gDecember 1, 2010

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

47%48%48%51%51%52%53%62%63%64%64%

68%71%72%

81%

60%

70%

80%

90%

100%

International Order Acceptance in 2009

% of Merchants Accepting International Orders From…

Over half of merchants accept online orders fromoutside the U.S. and/or Canada**

In 2009 these orders represented on average 21%of total orders up from 17% in 2008

hant

s

Average # ofCountries per Merchant

9

3

47%48%48%

0%

10%

20%

30%

40%

50%

UnitedKingdom

Australia Germany France Italy Mexico Spain Japan HongKong

Singapore Brazil China SouthKorea

Taiwan India

Q4b. From which of the following countries, outside the U.S. and Canada,do you accept online orders? Please select all that apply.

Results < 25% not shown

Base: Merchants accepting international orders

**Note: 54% in 2009; 52% in 2008, 59% in 2007

Note: A list of countries was provided, but merchants were also allowed to add any countrythat was missing from the list. (The list of countries provided changed in 2008.)

n=191

%of

Mer

ch

RulesOrders

Chargeback

Reject

Detectors

Automated Screening

Fraud Management Process

Management

Tuning &Analytics

Manual Review

Fraud Rates in U.S./Canada(Overall and by Online Segment)

Rat

e2%

Overall Digital Goods/Svcs

Media &Entertainment

Apparel/Jewelry

Health ConsumerElectronics

Household &General

Merchandise

Education/Government

Inte

rnat

iona

lFra

udR

Source: 2010 CyberSource Fraud Report

16%Manual Review

Top Priority Strategy / Area of Focus 2010

60%AutomatedDetection

(tasks / workflow)

20%ProcessAnalytics

2% Outsourcing2% Other Source: 2010 CyberSource Fraud Report

RulesOrders

ChargebackManagement

Reject

Detectors

Automated Screening

# Detection Tools = 7

g

Tuning &Analytics

Manual Review

50% say“Fraud is cleaner”

86%35%

16%33%

80%

10%4%

24%

3%

12%12%

5%17%

14%

9%

5%

CVN (Card Verification Number)Address Verification Service

Postal address validation servicesVerified by Visa/MasterCard SecureCode

Telephone # verification/reverse lookupPaid for public records services

Credit history checkOut-of-wallet or in-wallet challenge/response

Automated Fraud Detection Tool UseFraud Detection Tool Usage

% Currently Using

% Planning to Implement

Merchants $25M+ Online Revenue2009

Validation Services

Your Proprietary Data/Customer History

75%66%

53%41%

19%

52%

23%

26%45%

61%

18%

19%

6%

19%

12%

5%

12%

10%

17%14%

9%

19%

Customer order historyNegative lists (in-house lists)

Order velocity monitoringFraud scoring model-company specific

Positive listsCustomer website behavior analysis

IP geolocation informationDevice "fingerprinting"

Shared negative lists-shared hotlistsMulti-merchant purchase velocity

Other

Purchase Device Tracing

Multi-Merchant Data/Purchase History

Validation Services

No Silver Bullet% Merchants Using Tool that Selected it as

One Of Their “Top Three” Most Effective2009

26%20%

19%16%

9%

16%15%

10%

2%

32%Paid for public records servicesContact customer to verify order

Credit history checkVerified by Visa/MasterCard SecureCode

Address Verification ServiceCVN (Card Verification Number)

Telephone # verification/reverse lookupOut-of-wallet or in-wallet challenge/response

Postal address validation servicesContact card issuer/Amex CVP

Your Proprietary Data/Customer History

Purchase Device Tracing

Multi-Merchant Data/Purchase History

Q10c. Of the tools your company currently uses to help detect online payment fraud or assessfraud risk for online orders, please select the most effective. Please select up to three.

Base: Merchants with annual online sales ≥$25M who use tool : automated or manual (excludes None)

*Caution: small base

37%31%

22%16%

7%

22%

21%

36%

11%

14%

Fraud scoring model-company specificNegative lists (in-house lists)

Customer website behavior analysisCustomer order history

Order velocity monitoringPositive lists

IP geolocation informationDevice "fingerprinting"

Multi-merchant purchase velocityShared negative lists-shared hotlists

Protect• Keep more revenue• Keep brand safe

Optimize• Operate with less complexity/cost• Access better analytics to manage

BusinessImprovements

Simplifying Payment Management

Grow• Reach more customers, faster• Change/add without disruption

10

Screening Rules UI

Risk Analysis

Screening Rules UI

Case Management UI

Reporting & Analytics UI

RulesOrders

ChargebackManagement

Reject

Detectors

Automated Screening

Management

Tuning &Analytics

Manual Review

WebsiteCall Center / IVR

BatchPoint Of Sale

Credit & Debit CardsGift & Pre-Paid Cards

eChecks & Direct DebitsPayPal & BML

Payment Types Sales Channels

Technology Partners:

DATA QUALITY

Data Correlation Provides Fraud Intelligence

• 15 years experience• Billions of transactions modelled• Over 200 tests applied to every transaction

Output Example

Score 0-99

F t C d F (F d Li t)Increasing

• Merchants marking suspicious transactions• Reviewer decisions• Chargeback automarking by banks• Partnership with Visa

Factor Codes(> 20)

F (Fraud List)G (Geolocation inconsistency)N (Nonsensical input)

Info Codes(>125)

MM-BIN (BIN mismatch)UNV-ADDR (unverifiable address)VEL-NAME (multiple names with card)

ginsight

No ‘black box’

Identity Morphing Detection

Your Order

Mary Smith4XXXXXX0453mary@gmail com

Tricia Lim4XXXXXXXX0453spirit@yahoo.co.ukD-Fingerprint: XYZ

Home Depot

Air Canada

Tricia Lim4XXXXXXXX0123woti@aol.comD-Fingerprint: ABC

TAM

Timberland

Global,multi-merchant

intelligenceName changes: MultipleCredit cards: MultipleEmail changes: MultipleDevices: Multiple

Name changes: MultipleCredit cards: MultipleEmail changes: MultipleDevices: Multiple

ResultsResults

mary@gmail.comD-Fingerprint: ABC

Adam Jones4XXXXXXXX0453saab@hotmail.inD-Fingerprint: XYZ

Nike

Imran Cochin5XXXXXXXX7395saab@hotmail.inD-Fingerprint: ABC

Tricia Lim4XXXXXXXX6329devil@gmail.comD-Fingerprint: QRS

Pacific Sunwear

Pablo Jimenez4XXXXXXXX6329pablo@yahoo.comD-Fingerprint: XYZ

RulesOrders

ChargebackManagement

Reject

Detectors

Automated Screening

g

Tuning &Analytics

Manual Review

Case Management with One-Click Validation

+

RulesOrders

ChargebackManagement

Reject

Detectors

Automated Screening

g

Tuning &Analytics

Manual Review

Reporting and Analytics

Performance Reports on:• Screening Profile• Rules• Review Process

Fraud Screen Flow – Using CyberSource

Order

BusinessRules

-Flexible

UserConsole

Act

ive

Pas

sive

Accept/Reject Decision

4D Validation

Review

CaseManagement

Performance Management• Strategy Design• Process Optimization• Rule Tuning• Reviewer Performance

Reporting Analytics Insight

The MostWidely UsedOnline FraudManagement

Solution,Solution,Worldwide

Airline Partners

Fraud Management Expertise

• Since 1995• Global, multi-merchant view of fraud trends• Secure, reliable, trusted public company

Thousands ofmerchants

globally

• Board Member: Merchant Risk Council - USA• Board Member: Merchant Risk Council - EU• Member: PCI Security Standards Council

Active industryleadership

• Trainer (US): NSA, CIA, FBI• Advisor (UK): Shadow Home Affairs Minister• Annual fraud report + airline fraud report• Long-standing Visa partnership on fraud

Trusted advisor

Asia: 2000• CyberSource K.K. established 2000• JV with Trans-Cosmos, Inc.• Sales, Marketing, Support, Operations• Datacenter: Tokyo

Global Presence

USA: 1997• HQ: Mountain View, CA• Offices throughout US• Engineering, Operations, Sales, Marketing, Admin• Datacenters: Arizona, California, Colorado, Washington

Europe: 1997• HQ: UK• Sales, Marketing, Support, Operations• Datacenter: London• Engineering: Belfast, Northern Ireland

Acquired by Visa: July 2010

Gracias!

Kathy ReevesKreeves@CyberSource.com

+1.817.291.4499

Daryl WilliamsDwilliams@CyberSource.com+1.770.917.1193