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CYBERSOURCE FRAUD

MANAGEMENT

e-Commerce Day

Bogota, Colombia

Daryl Williams

Manager, Sales Engineer

[email protected]

Kathy Reeves

Business Development Manager

[email protected]

g

(2)
(3)

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 from

outside the U.S. and/or Canada**

In 2009 these orders represented on average 21%

of total orders up from 17% in 2008

h

a

n

ts

Average # of Countries per Merchant

9

3

47% 48% 48% 0% 10% 20% 30% 40% 50% United Kingdom

Australia Germany France Italy Mexico Spain Japan Hong Kong

Singapore Brazil China South Korea

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 country that was missing from the list.(The list of countries provided changed in 2008.)

n=191

%

o

fM

e

rc

h

(4)

Rules

Orders

Chargeback

Reject

Detectors

Automated Screening

Fraud Management Process

Management

Tuning &

Analytics

Manual Review

(5)

Fraud Rates in U.S./Canada

(Overall and by Online Segment)

R

a

te

2

%

Overall

Digital Goods/

Svcs

Media &

Entertainment

Apparel/

Jewelry

Health

Consumer

Electronics

Household &

General

Merchandise

Education/

Government

In

te

rn

a

ti

o

n

a

lF

ra

u

d

R

(6)

16%

Manual Review

Top Priority Strategy / Area of Focus 2010

60%

Automated

Detection

(tasks / workflow)

20%

Process

Analytics

2% Outsourcing

(7)

Rules

Orders

Chargeback

Management

Reject

Detectors

Automated Screening

# Detection Tools = 7

g

Tuning &

Analytics

Manual Review

50% say

“Fraud is cleaner”

(8)

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 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

Automated Fraud Detection Tool Use

Fraud Detection Tool Usage

% Currently Using % Planning to Implement

Merchants $25M+ Online Revenue

2009

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 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

Purchase Device Tracing Multi-Merchant Data/Purchase History

(9)

Validation Services

No Silver Bullet

% Merchants Using Tool that Selected it as

One Of Their “Top Three” Most Effective

2009

26% 20% 19% 16% 9% 16% 15% 10% 2% 32%

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

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 assess fraud risk for online orders, please select the most effective. Please select up to three.

Base: Merchants with annual online sales ³$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 specific Negative lists (in-house lists) Customer website behavior analysis Customer order history Order velocity monitoring Positive lists

IP geolocation information Device "fingerprinting"

Multi-merchant purchase velocity Shared negative lists-shared hotlists

(10)

Protect

• Keep more revenue

• Keep brand safe

Optimize

• Operate with less complexity/cost

• Access better analytics to manage

Business

Improvements

Simplifying Payment Management

Grow

• Reach more customers, faster

• Change/add without disruption

(11)

Screening Rules UI

Risk Analysis

Screening Rules UI

Case Management UI

(12)

Rules

Orders

Chargeback

Management

Reject

Detectors

Automated Screening

Management

Tuning &

Analytics

Manual Review

(13)

Website

Call Center / IVR

Batch

Point Of Sale

Credit & Debit Cards

Gift & Pre-Paid Cards

eChecks & Direct Debits

PayPal & BML

Payment Types

Sales Channels

Technology Partners:

(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

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)

g

insight

No ‘black box’

(15)

Identity Morphing Detection

Your Order

Mary Smith

4XXXXXX0453

mary@gmail com

Tricia Lim

4XXXXXXXX0453

[email protected]

D-Fingerprint: XYZ

Home Depot

Air Canada

Tricia Lim

4XXXXXXXX0123

[email protected]

D-Fingerprint: ABC

TAM

Timberland

Global,

multi-merchant

intelligence

Name changes: Multiple

Credit cards: Multiple

Email changes: Multiple

Devices: Multiple

Name changes: Multiple

Credit cards: Multiple

Email changes: Multiple

Devices: Multiple

Results

Results

[email protected]

D-Fingerprint: ABC

Adam Jones

4XXXXXXXX0453

[email protected]

D-Fingerprint: XYZ

Nike

Imran Cochin

5XXXXXXXX7395

[email protected]

D-Fingerprint: ABC

Tricia Lim

4XXXXXXXX6329

[email protected]

D-Fingerprint: QRS

Pacific Sunwear

Pablo Jimenez

4XXXXXXXX6329

[email protected]

D-Fingerprint: XYZ

(16)

Rules

Orders

Chargeback

Management

Reject

Detectors

Automated Screening

g

Tuning &

Analytics

Manual Review

(17)

Case Management with One-Click Validation

(18)

Rules

Orders

Chargeback

Management

Reject

Detectors

Automated Screening

g

Tuning &

Analytics

Manual Review

(19)

Reporting and Analytics

Performance Reports on:

• Screening Profile

• Rules

(20)

Fraud Screen Flow – Using CyberSource

Order

Business

Rules

-Flexible

User

Console

A

ct

iv

e

P

a

ss

iv

e

Accept/Reject

Decision

4D Validation

Review

Case

Management

Performance Management

• Strategy Design

• Process Optimization

• Rule Tuning

• Reviewer Performance

(21)

The Most

Widely Used

Online Fraud

Management

Solution,

Solution,

Worldwide

(22)
(23)

Fraud Management Expertise

• Since 1995

• Global, multi-merchant view of fraud trends

• Secure, reliable, trusted public company

Thousands of

merchants

globally

• Board Member: Merchant Risk Council - USA

• Board Member: Merchant Risk Council - EU

• Member: PCI Security Standards Council

Active industry

leadership

• Trainer (US): NSA, CIA, FBI

• Advisor (UK): Shadow Home Affairs Minister

• Annual fraud report + airline fraud report

• Long-standing Visa partnership on fraud

(24)

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

(25)

Gracias!

Kathy Reeves

[email protected]

+1.817.291.4499

Daryl Williams

[email protected]

+1.770.917.1193

References

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