Experts predict that EMV adoption in the US will set off a tsunami of card not present fraud. Are you leveraging everything you've got in preparation for the oncoming threat? Find out in this Practical Guide.

1. A PRACTICAL GUIDE
to post-EMV card-not-present fraud
Noam Inbar, VP Business Development, Forter

2. $3 BILLION
2014 U.S. CNP Credit Card Fraud Losses (Aite Group)

3. EMV will make your fraud disappear
HRS.
0 9
MIN.
3 0
DAYS
9 9

4. REALLY?

5. NOT REALLY.
1 Being a fraudster is profession.
EMV won’t make them disappear 2
Fraudsters look for the weakest link;
EMV doesn’t protect Card Not
Present Transactions
4 E-commerce will continue
to grow3
EMV migration will cause
organizations to be slower and less
efficient than before
5 Crime as a service: even fraudsters with low technical abilities can commit
fraud online, lower barriers to entry

6. FRAUD TO SPIKE 40-50%
In the 2 years following EMV migration
Research

7. WELCOME TO THE POST
EMV FRAUD TSUNAMI

8. DOMINANT MARKET APPROACH
to fraud prevention
Rule Engine Risk Score Fraud Policies
Manual Reviews
APPROVE
DECLINE

9. DOMINANT MARKET APPROACH
to fraud prevention
Rule Engine Risk Score Fraud Policies
Manual Reviews
APPROVE
DECLINE
FRAUD
PREVENTION
1.0

10. 2.0 FRAUDSTERS
Require 2.0 Fraud Protection

11. MACHINE LEARNING – BIG DATA – CLO
REALTIME ALGORITHIMS – SCORES – R
ENGINES – FINGERPRINTING – MACHIN
LEARNING GEOLOCATION – CLOUD – R
– BLACKLISTS – BEHAVIORAL – ALGOR
MACHINE LEARNING – CLOUD – SCORE
FINGERPRINTING – BLACKLISTS – BIG D
SCORES – REALTIME ALGORITHIMS – B

12. A PRACTICAL GUIDE
to post-EMV card-not-present fraud

13. 1
KYF: KNOW YOUR FRAUDSTER

14. FRAUD IS CHANGING
So should your fraud prevention
3 Fraudsters are quick and agile, methods that
used to be the holy grail of fraud prevention
can no longer get the job done
Traditional Practices are no longer enough
1 Dark-net Marketplaces enable a sophisticated
fraud ecosystem
Crime as a Service
5 Wherever there’s internet, there’s the
opportunity for CNP fraud
Fraud is Global
4 After Silk Road’s demise, fraudsters have
become vigilant about operation security
Fraudsters Are Paranoid
2 2014’s massive data breaches flooded the
market with high quality cards
Abundance of Stolen Data
6 Hardware is cheaper than ever, so fraudsters
can burn through it & never look back
Hardware is Commoditized

15. 2
AUTOMATE

16. 81%
of merchants
review orders manually
52%
of fraud budget is used for
manual reviews
MANUAL REVIEWS
20+ MIN
Per a manual review, for over
20% of merchants
Source: Cybersource Online Fraud Report

17. Nuances and patterns extracted from a user’s online behavior
enables comparing and benchmarking against expected behaviors,
adding a whole new dimension of knowledge.
BEHAVIORAL ANALYSIS
Automating manual reviews
Predicting people is not like predicting the weather

18. 3
DON’T PANIC

19. FALSE POSITIVES
| Definition |False Positives
A "false positive,"... arises
when fraud detection software
blocks your card because the card has been identified as
the vehicle of potentially fraudulent activity when it isn’t
~ Tech Republic

20. FALSE POSITIVES
$40 BILLION
lost every year due to unnecessary red flags
and transaction blocks
Source: Trust Insight, Measuring Consumer Attitude on CNP Credit Card Declines Report

21. FALSE POSITIVES
Source: Cybersource Online Fraud Management Benchmark Study (N. American edition, published 2015), Ethoca research 2015
OVER 70%
of merchants believe that
UP TO 10%
of rejected orders are actually valid
BUT THE ACTUAL RATE IS ESTIMATED AT ABOVE 40%!

22. FALSE POSITIVES
NEARLY 20%
of consumers who experienced a fraud-related decline
had no future spend 6 months after the decline event
Source: Trust Insight, Measuring Consumer Attitude on CNP Credit Card Declines Report

23. FALSE POSITIVES - CAUSES
§ Processor rules and red flags
§ Tools that require hard coding
§ Outdated rules
§ Manual reviews: bias

24. EXAMPLE: AIRLINE

25. 3DSECURE DECLINED

26. MANUAL REVIEW EMAIL

27. APPROVED BY PHONE WITH SAME CARD

28. 4
HUMAN-BASED MACHINE LEARNING

29. MAN VS. THE MACHINE

30. EXPERT KNOWLEDGE
Interdependencies: What do the data points tell us?
Platinum+
Credit Card Type
San Jose, US
Billing Neighborhood
Mexico (very low income)
Shipping Neighborhood
$200, $90, $80
Past Purchase Amounts
$10,000
Current Purchase Amount
Spanish
Browsing Language
Wireless Network
IP Type

31. Platinum+
Credit Card Type
San Jose, US
Billing Neighborhood
Mexico (very low income)
Shipping Neighborhood
$200, $90, $80
Past Purchase Amounts
$10,000
Current Purchase Amount
Spanish
Browsing Language
Wireless Network
IP Type
EXPERT KNOWLEDGE
Stories Model: Mexican National Holiday Sale
Immigrant shipping to family

32. 5
SMART LINKING

33. UNCOVER THE FRAUDSTER SOCIAL GRAPH
Verification and authentication of a single transaction and blacklists that are based
on IP match and email match provide a very narrow view
Similarities and proximities reveal beyond the transaction

35. 1. KNOW YOUR FRAUDSTER
2. AUTOMATE
3. DON’T PANIC
4. HUMAN BASED MACHINE LEARNING
5. SMART LINKING
RECAP: WHAT TO DO

36. GOOD LUCK!
www.forter.com noam@forter.com @InbarNoam