Beat Fraud While Increasing Profits - PYMNTS.com€¦ · reviewer productivity © 2011 CyberSource....
Transcript of Beat Fraud While Increasing Profits - PYMNTS.com€¦ · reviewer productivity © 2011 CyberSource....
© 2011 CyberSource. All rights reserved.
Beat Fraud While Increasing Profits
Scott Boding
Chris Holmes
October 4, 2011
© 2011 CyberSource. All rights reserved.
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
© 2011 CyberSource. All rights reserved.
*Source: eMarketer
Online holiday sales growth
% Holiday sales of your total sales
Economic uncertainty
Current Situation
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Current Situation
Are you ready for higher
customer traffic this holiday
season?
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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?
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Current Situation
How do you tell the good
customers from the bad ones?
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What are your options?
# of
reviews
Customer
satisfaction Fraud rate Overhead Profit
Status quo
Staff up
Accept more
Work smarter
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Your best option
Order acceptance
Customer satisfaction
# cases reviewed
Fraud rate /
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Your best option
Order acceptance
Customer satisfaction
grows your bottom line
# cases reviewed
Fraud rate /
$$$
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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
Tests & Data History
Get the
most data Orders
Orders
Gather data
intelligence
Maximize
reviewer
productivity
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Order Disposition
Case Study: Electronics Retailer
Reject
Accept
Orders
Case Management
Fraud
Business Rules
20 bps
Tests & Data History
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
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Order Disposition
Case Study: Electronics Retailer
Reject
Accept
Orders
Case Management
Fraud
Business Rules
20 bps
Tests & Data History
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)
© 2011 CyberSource. All rights reserved.
MANUAL
AUTOMATED
Order Disposition
Solution: Fine tune your fraud management
Reject
Accept
Orders
Case Management
Fraud
Business Rules
Tests & Data History
Get the
most data Orders
Orders
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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
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Case Study: Electronics Retailer
…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
© 2011 CyberSource. All rights reserved.
MANUAL
AUTOMATED
Order Disposition
Solution: Fine tune your fraud management
Reject
Accept
Orders
Case Management
Fraud
Business Rules
Tests & Data History
Get the
most data Orders
Orders
Gather data
intelligence
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Gather Data Intelligence
Anomalies Business
Rules
•Accept
•Reject
•Review
Correlation Model
Order Data
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Gladys Smith
4XXXXXX0123
D-Fingerprint: ABC
Retailer 1
Gladys Smith
4XXXXXX0123
D-Fingerprint: ABC
Retailer 2
Tricia Lim
4XXXXXX9234
D-Fingerprint: ABC
Retailer 3
Yip Lim
4XXXXXX0123
D-Fingerprint: XYZ
Retailer 4
Tricia Lim
4XXXXXX9234
D-Fingerprint: XYZ
Herman Stutz
4XXXXXX1454
Tricia Lim
4XXXXXX9234
D-Fingerprint: XYZ
Retailer 1
Maricella Mendoza
4XXXXXX1454
D-Fingerprint: XYZ
Retailer 4
Farad Shah
4XXXXXX9234
Retailer 5
Name changes: Multiple
Credit cards: Multiple
Email changes: Multiple
Identities/Device: Multiple
Results
Gather Data Intelligence
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Case Study: Electronics Retailer
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
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MANUAL
AUTOMATED
Order Disposition
Solution: Fine tune your fraud management
Reject
Accept
Orders
Case Management
Fraud
Business Rules
Tests & Data History
Get the
most data Orders
Orders
Gather data
intelligence
Optimize
business rules
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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
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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
Case Study: Electronics Retailer
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MANUAL
AUTOMATED
Order Disposition
Solution: Fine tune your fraud management
Reject
Accept
Orders
Case Management
Fraud
Business Rules
Minimize
cases
reviewed
Optimize
business rules
Tests & Data History
Get the
most data Orders
Orders
Gather data
intelligence
Maximize
reviewer
productivity
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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
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Evaluate Results & Tune
• Did you achieve your
objectives?
• How do you measure?
• What do you do next?
Order acceptance
Customer satisfaction
# cases reviewed
Amount of fraud /
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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
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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
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Order Disposition
Case Study: Electronics Retailer – BEFORE
Reject
Accept
Orders
Case Management
Fraud
Business Rules
20 bps
Tests & Data History
Projected
Increase:
10 – 12%
Orders
Orders
Average
Ticket Size:
$650
Manual
Review: 38%
No staff
increase
Order review
backlog:
4 – 6 days
© 2011 CyberSource. All rights reserved.
MANUAL
AUTOMATED
Order Disposition
Case Study: Electronics Retailer – AFTER
Reject
Accept
Orders
Case Management
Fraud
Business Rules
20 bps
Tests & Data History
Actual
Increase:
19%
Orders
Orders
Average
Ticket Size:
$650
Manual
Review: 16%
No staff
increase
Order review
backlog: < 24
hours
58%
83%
58%
SAME
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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
Customer satisfaction
# cases reviewed
Amount of fraud /
© 2011 CyberSource. All rights reserved.
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
© 2011 CyberSource. All rights reserved.
For more information
Call us 1-888-330-2300
www.cybersource.com