A Practical Guide to Post-EMV Card Not Present Fraud

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

Noam Inbar, VP Business Development, Forter

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

EMV will make your fraud disappear

HRS.

0 9 MIN.

3 0 DAYS

9 9

REALLY?

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 grow 3

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

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

Research

WELCOME TO THE POST EMV FRAUD TSUNAMI

DOMINANT MARKET APPROACH to fraud prevention

Rule Engine Risk Score Fraud Policies

Manual Reviews

APPROVE

DECLINE

DOMINANT MARKET APPROACH to fraud prevention

Rule Engine Risk Score Fraud Policies

Manual Reviews

APPROVE

DECLINE

FRAUD PREVENTION

1.0

2.0 FRAUDSTERS Require 2.0 Fraud Protection

MACHINE LEARNING – BIG DATA – CLOUD REALTIME ALGORITHIMS – SCORES – RULE ENGINES – FINGERPRINTING – MACHINE LEARNING GEOLOCATION – CLOUD – REALTIME – BLACKLISTS – BEHAVIORAL – ALGORITHIMS – MACHINE LEARNING – CLOUD – SCORES –FINGERPRINTING – BLACKLISTS – BIG DATA – SCORES – REALTIME ALGORITHIMS – BLACKL

A PRACTICAL GUIDE

to post-EMV card-not-present fraud

1 KYF: KNOW YOUR FRAUDSTER

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

2 AUTOMATE

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

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

3 DON’T PANIC

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

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

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%!

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

FALSE POSITIVES - CAUSES

§  Processor rules and red flags §  Tools that require hard coding §  Outdated rules §  Manual reviews: bias

EXAMPLE: AIRLINE

3DSECURE DECLINED

MANUAL REVIEW EMAIL

APPROVED BY PHONE WITH SAME CARD

4 HUMAN-BASED MACHINE LEARNING

MAN VS. THE MACHINE

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

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

5 SMART LINKING

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

1.  KNOW YOUR FRAUDSTER 2.  AUTOMATE 3.  DON’T PANIC 4.  HUMAN BASED MACHINE LEARNING 5.  SMART LINKING

RECAP: WHAT TO DO

GOOD LUCK! www.forter.com [email protected] @InbarNoam