1.
Monica Eaton-Cardone
Monica Eaton-Cardone is an entrepreneur and business leader with expertise
in technology systems and threat metrics, product development, e-
commerce retention, risk relativity and agile process design. She has co-
founded a number of successful companies—including Chargebacks911,
eConsumerServices and Global Risk Technologies—and currently fulfills
executive management roles within each organization. Eaton-Cardone has
earned a reputation for developing effective and innovative business
solutions, helping merchants and banks to achieve maximum sustainability
while supporting consumers in resolving transaction issues in order to main
greater balance. Eaton-Cardone's deep understanding of the digital
transaction life cycle and unique value chain ecosphere, provides the
company with unparalleled knowledge and skill set.

2.
Affiliate Fraud Triggers You’re Ignoring …But Shouldn’t

3.
 Your website traffic is off the charts
 Your conversion rate is awesome
 You have tons of affiliates and publishers knocking at your door
 Your bank account is getting rich
 You are generating so many leads
You think your campaign is going well because…

4.
It is time to celebrate…right?!

5.
It is time to celebrate…right?!
WRONG!!!
Believe it or not – each of those items can be a sign
for AFFILIATE FRAUD
Before you know it…YOU’VE BEEN PUNKED!
(scammed, defrauded, taken for a ride and tricked)

6.
Affiliate fraud patterns are difficult to see…

7.
Fraud Filters
• Fraud filters detect credit card fraud, but
affiliate fraud is different
• Fraud filters aren’t equipped to detect
affiliate fraud
• Adjusting fraud filter rules in an attempt
to make them work for affiliate fraud
will increase false positives

8.
Repercussions of Fraud
Obvious repercussions of fraud…
• Chargebacks
• Decreased rebills for subscription
products
• Increased refunds
• Decreased potential for good revenue
Unsuspected side effects…
• Increased conversion rates
• Efforts to increase profitability further damage
the customer relationship and yield greater
long-term losses

9.
Managing Affiliate Fraud Risks

10.
Understanding Fraud
Affiliates Criminals Consumers
Adware
Cookie-stuffing
Loyalty plugins
Typo squatting
iFrame
Post back URL
Discount Codes
Incentives
Clean fraud
Account takeover
New account fraud
Credit card fraud
Friendly fraud
Family fraud
Accidental fraud
300% GROWTH
10% LATERAL GROWTH
41% VERTICAL GROWTH
LARGEST SOURCE OF ADVERTISER FRAUD
3 Sources, behind the scenes growth-rates

11.
Review online traffic
8% growth in traffic is suspicious for non-coupon sites

12.
Speak to your customers

13.
Survey for Incentive Traffic

14.
Offer profiling
demographic comparison analysis

15.
Blacklist fraudulent websites
…but don’t relax.

16.
Manage your conversion
Geo-location, device intelligence, online behavior patterns, comparative trending analysis

17.
Use A/B Scoring
< A vs. B >
A is the control & B is the test

18.
Comparative Analysis = KPIs
Define and Measure your KPIs
 Quick call rates
 Abandonment rates
 Conversion rates – click through rates
 Decline ratios
 Demographic
(who is your typical customer….have they changed?)
 Refund rates
 Google alert activity

19.
Best Practices (for mitigating fraud)
Regardless of the type of fraud, all mitigation strategies must include the following:
• A multi-layer approach
• Dynamic, evolving techniques
• Professional tools
• A genuine understanding of the actual fraud sources
• A/B testing to establish KPIs
• Use experts (mistakes are very expensive)
• Continuous measurement, inspection, learning

20.
AND…Explore New Innovative Tools
Based on our own experience as advertisers…
• Traditional fraud filters are equipped to handle credit card fraud—not affiliate fraud.
• Adjusting fraud filter rules in an attempt to detect affiliate fraud results in increased false positives by
as much as 72%.
Therefore…advertisers need a complimentary tool that’s designed
specifically for affiliate fraud.
• Detect fraud to reduce costs and optimize profitability
• Qualify a company that has had experience as a merchant
• Machine learning and Human Review solutions are 235% more successful than automation alone