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© 2011 CyberSource. All rights reserved.

Beat Fraud While Increasing Profits

Scott Boding

Chris Holmes

October 4, 2011


SPEAKERS

Scott Boding

Sr. Business Leader, Order Screening

• 12 years of experience in the development of anti-

fraud strategies and rule making.

• Leading authority on the architecture of fraud and

fraud abatement.

Chris Holmes

Manager, Managed Services

• Fought fraud for some of the largest e-Commerce

brands in the world

• Over 9 years of experience in fraud management.

• Manage team of fraud analysts


*Source: eMarketer

Online holiday sales growth

% Holiday sales of your total sales

Economic uncertainty

Current Situation


Current Situation

Are you ready for higher

customer traffic this holiday

season?


Holiday Math

*CyberSource 2011 Annual Online Fraud Report


Holiday Math

Do More With Less!


Order Disposition

Current Situation

Reject

Accept Orders

Case Management

Fraud

Business Rules

9-35% manual review

76% accept rate

2-6%

Tests & Data History

0.9%

“Cleaner”

*CyberSource 2011 Annual Online Fraud Report

Are these metrics acceptable

to you this holiday season?


Current Situation

How do you tell the good

customers from the bad ones?


What are your options?

# of

reviews

Customer

satisfaction Fraud rate Overhead Profit

Status quo

Staff up

Accept more

Work smarter


Your best option

Order acceptance

Customer satisfaction

# cases reviewed

Fraud rate /


Your best option

Order acceptance


grows your bottom line

# cases reviewed

Fraud rate /

$$$


MANUAL

AUTOMATED

Order Disposition

Solution: Fine tune your fraud management

Reject

Accept

Orders

Case Management

Fraud

Business Rules

Keep

same

Minimize

orders

reviewed

Keep

same

Optimize

business rules


Get the

most data Orders

Orders

Gather data

intelligence

Maximize

reviewer

productivity


Order Disposition

Case Study: Electronics Retailer

Reject

Accept

Orders

Case Management

Fraud

Business Rules

20 bps


Projected

Increase:

10 – 12%

Orders

Orders

Average

Ticket Size:

$650

Manual

Review: 38%

No staff

increase

Order review

backlog:

4 – 6 days

REVENUE AT RISK: $2.6 MILLION


Order Disposition


Reject

Accept

Orders

Case Management

Fraud

Business Rules

20 bps


Projected

Increase:

10 – 12%

Orders

Orders

Average

Ticket Size:

$650

Manual

Review: 38%

No staff

increase

Order review

backlog:

4 – 6 days

REVENUE AT RISK: $2.6 MILLION

Reduce manual review

Maintain fraud level

Keep customers happy

(decision orders < 36 hours)


MANUAL

AUTOMATED

Order Disposition


Reject

Accept

Orders

Case Management

Fraud

Business Rules


Get the

most data Orders

Orders


ORDER DETAILS

• Sales Channel

• Product Group Risk

• Shipping Method

• Vertical-Specific

ID INFO

• Valid Ship to Address

• Valid Name

• Public Records

• Credit Report

• AVS/CVN

• Device Behavior

PURCHASE HISTORY

• Purchase Velocity

• Linked to Card Testing

• Negative List

• Chargebacks

• Single Merchant

• Multi-Merchant

Get the Most Data

Types of data to obtain

……More Data Leads to More Fraud Detection



…and more

ORDER DETAILS

• Sales Channel

• Product Group

Risk

• Shipping Method

• Vertical-Specific

ID INFO

• Valid Ship to

Address

• Valid Name

• Public Records

• Credit Report

• AVS/CVN

• Device &

Network

PURCHASE

HISTORY

• Purchase

Velocity

• Linked to Card

Testing

• Negative List

• Chargebacks

• Single Merchant

• Over 60 Billion Visa + CyberSource

managed transactions annually

• Results of over 200 Fraud Detection

Tests

• Multi-Merchant Data

• Purchase Velocity

(frequency,

cumulative $/units)

• Chargeback/Truth

Data

• Link to card testing

• IP Geolocation

• Device fingerprinting

• Packet Signature

Inspection

• BIN Analysis


MANUAL

AUTOMATED

Order Disposition


Reject

Accept

Orders

Case Management

Fraud

Business Rules


Get the

most data Orders

Orders

Gather data

intelligence


Gather Data Intelligence

Anomalies Business

Rules

•Accept

•Reject

•Review

Correlation Model

Order Data


Gladys Smith

4XXXXXX0123

[email protected]

D-Fingerprint: ABC

Retailer 1

Gladys Smith

4XXXXXX0123

[email protected]

D-Fingerprint: ABC

Retailer 2

Tricia Lim

4XXXXXX9234

[email protected]

D-Fingerprint: ABC

Retailer 3

Yip Lim

4XXXXXX0123

[email protected]

D-Fingerprint: XYZ

Retailer 4

Tricia Lim

4XXXXXX9234

[email protected]

D-Fingerprint: XYZ

Herman Stutz

4XXXXXX1454

[email protected]

Tricia Lim

4XXXXXX9234

[email protected]

D-Fingerprint: XYZ

Retailer 1

Maricella Mendoza

4XXXXXX1454

[email protected]

D-Fingerprint: XYZ

Retailer 4

Farad Shah

4XXXXXX9234

[email protected]

Retailer 5

Name changes: Multiple

Credit cards: Multiple

Email changes: Multiple

Identities/Device: Multiple

Results

Gather Data Intelligence



Chargeback and Business Rules Analysis for the Holiday

Season

• Chargebacks + Bill To / Ship To mismatch: $1,000

• Chargebacks + IP/ Billing state mismatch + low ticket: N/A

• Coupon code usage: likely good customer

• Maximum order value: $2000


MANUAL

AUTOMATED

Order Disposition


Reject

Accept

Orders

Case Management

Fraud

Business Rules


Get the

most data Orders

Orders

Gather data

intelligence

Optimize

business rules


REVIEW ACCEPT

Optimize Your Business Rules

Rule building strategy

• Build auto-accept/auto-reject rules based on common attributes

• Modify rules for further review

REJECT

Commonly

Accepted

Commonly

Rejected

Missed

Fraud

False

Positives


REJECT

High Risk

• Hidden proxy

• IP / Billing time zone

mismatch

• Restricted countries

• High-risk SKU + no AVS

• Identity velocity

EVALUATE

Change Rule

Parameters

• Change Bill/ship to

mismatch to $1000

• Remove state mismatch

for low ticket items

• Add coupon usage rule

• Relax expedited shipping

rules

ACCEPT

Low Risk

• Good order detail

• Validated payment data

• Validated purchaser data

• Valid device/network info

• No chargeback history

• Loyalty program usage

• Good purchase history

Categorize Rules



MANUAL

AUTOMATED

Order Disposition


Reject

Accept

Orders

Case Management

Fraud

Business Rules

Minimize

cases

reviewed

Optimize

business rules


Get the

most data Orders

Orders

Gather data

intelligence

Maximize

reviewer

productivity


Increase Process Efficiency

Streamline Case Management Workflow

• Flexible queue configuration

• Case routing

• Reviewer performance reports & analytics

Increase reviewer productivity

• Customizable UI

• Consolidated case information

• Integrated 3rd party callouts


Evaluate Results & Tune

• Did you achieve your

objectives?

• How do you measure?

• What do you do next?

Order acceptance


# cases reviewed

Amount of fraud /


Reporting and Analytics

• Measure results based on your

business needs

• Fraud rate

• Manual review rate

• Reject rate

• Review backlog…

• Analyze chargebacks and

incorporate into system

• Test new rules before going live

• Implement rules quickly

1046 922 9801109

7598 72

73

0

200

400

600

800

1000

1200

1400

7-Aug-11 14-Aug-11 21-Aug-11 28-Aug-11

Samashmusic Weekly Converted Orders July 31, 2011 - August 27, 2011

TotalRejects

TotalAccepts


Order Disposition

Case Study: Electronics Retailer – BEFORE

Reject

Accept

Orders

Case Management

Fraud

Business Rules

20 bps


Projected

Increase:

10 – 12%

Orders

Orders

Average

Ticket Size:

$650

Manual

Review: 38%

No staff

increase

Order review

backlog:

4 – 6 days


MANUAL

AUTOMATED

Order Disposition

Case Study: Electronics Retailer – AFTER

Reject

Accept

Orders

Case Management

Fraud

Business Rules

20 bps


Actual

Increase:

19%

Orders

Orders

Average

Ticket Size:

$650

Manual

Review: 16%

No staff

increase

Order review

backlog: < 24

hours

58%

83%

58%

SAME


Solution Requirements to Beat Fraud, Increase

Profits:

• Get the most data

– More data to detect fraud

– Multi-merchant data

– Verification data

– Chargebacks

• Gather intelligence

– Correlate data elements and

combinations of data

– Assess risk

• Optimize business rules

– Increase automated screening

– Fine-tune business rules

• Minimize manual review

– Streamline case management

– Improve productivity

• Evaluate results & tune

Order acceptance


# cases reviewed

Amount of fraud /


CyberSource Fraud Management Solutions

Achieve your objectives

Correlation Model & Rules System

Manual Review Services

CyberSource Decision Manager

Analytics

Expert Monitoring &

Consulting

Case Management

System

Managed Services

Expert Analysis With Passive

Mode Testing

The Most Data

• World’s largest fraud detection radar

• Multi-merchant data

Advanced intelligence

• Proven correlation model and risk

assessment

• Business user rules console

Flexible case management

• Flexible queue configuration

• Intuitive UI

• Integrated 3rd party call-outs

Reporting & Analytics

• Passive mode testing

• Auto marking of chargebacks

• Rule and reviewer performance

reports

Managed Risk Services

• Performance Monitoring

• Custom analysis

• Order Screening Management


For more information

Call us 1-888-330-2300

www.cybersource.com


Q & A

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