Cybercriminals have a variety of tools and techniques — as well as opportunities — to steal money and services. Furthermore, traditional fraud prevention tools often fail to stop this fraudulent activity. Companies need a new approach to fraud prevention — one that stops fraud early and preserves the user experience. Join our webinar, as we demonstrate how you can leverage next-generation fraud prevention to prevent more fraud while reducing costs and improving the user experience for trusted customers. Topics will include: Today's Fraudster: Examine the new tools and techniques cybercriminals are using to commit fraud Exploring Device Intelligence: Introduce the concepts of device recognition, reputation, and associations for blocking fraudulent activity Leveraging Human Insight and Machine Learning: Explain how human insight and machine learning are better together in the fight against fraud Harnessing the Power of Next-generation Device Intelligence: Explore the benefits of a next-generation approach to fraud prevention beyond simply catching more fraud Ten Buying Criteria for a Next-gen Fraud Prevention Solution: Enumerate criteria for choosing a next-gen fraud prevention solution Want more information on fraud prevention strategies that reduce costs from fraud loss while providing a positive user experience for trusted customers? Download our Definitive Guide to Next-generation Fraud Prevention eBook.

1. WEBINAR
DEFINITIVE GUIDE TO NEXT
GENERATION FRAUD
PREVENTION
NOVEMBER 2018
EDDIE GLENN

2. 2
EDDIE GLENN
F r a u d E x p e r t
a n d c o - a u t h o r o f
D e f i n i t i v e G u i d e t o N e x t G e n e r a t i o n
F r a u d P r e v e n t i o n
 25+ years in product management and product marketing,
with focus on online fraud prevention, safety-critical and risk-
prone software
 Authored articles for ITSP Magazine, Totally Gaming,
Gambling Insider, iGaming,

3. AGENDA
3
 How did we get here?
 Understanding the enemy
 Shortcomings of current fraud
prevention measures
 Next gen fraud prevention tools &
their benefits
 Buying criteria
 Q & A

4. 4
HOW DID WE GET HERE?

5. 5
HOW DID WE GET HERE?
Lack of anonymity
Physical presence required
Dangerous work
Jessie James
1847-1882
More than 20 bank & train robberies

6. 6
HOW DID WE GET HERE?
Lack of anonymity
Physical presence required
Dangerous work
1980’s
Fixed remote location
Limited access & options

7. 7
HOW DID WE GET HERE?

8. 8
HOW DID WE GET HERE?
During the 1990’s, the Internet went
from non-existent to over 17 million
websites
Anonymous
Many locations, but still
tethered
Real business conducted
online
Not always real-time

9. 9
ENTER THE MOBILE AGE
Real-time transactions
Global customer base
24/7/365 revenue
opportunities
New markets

10. 10
DANGERS LURK
BEHIND THE DEVICE
Fraudsters can work
anywhere, anytime
Anonymous
Real-time & repetitive
attacks

11. 11
IMPACT OF ONLINE FRAUD
ON BUSINESS
FRAUD
LOSSES
REVENUE
LOSSES
FRAUD PREVENTION
COSTS
CUSTOMER
ATTRITION
TARNISHED
REPUTATION
LEGAL &
REGULATORY
PENALTIES

12. 12
IMPACT OF ONLINE FRAUD
ON CONSUMERS
FINANCIAL
LOSSES
WASTED
TIME
DAMAGED
CREDIT
LOSS OF
BRAND CONFIDENCE

13. KNOW YOUR ENEMY
THE MANY FACES OF FRAUD

14. 14
WHO’S BEHIND FRAUD?
D I F F E R E N T T A C T I C S N E E D E D F O R D I F F E R E N T F R A U D S T E R S
Unintentional
Fraud
First Party
Fraud
Synthetic Identity
Fraud
Stolen Identity
Fraud

15. 15
COMMON TYPES OF FRAUD
This is what most people think of
when asked about online fraud…
…and is a problem for almost
every industry…
…but is not the only type to be
concerned with…

16. 16
COMMON TYPES OF FRAUD
“ B E F O R E Y O U K N O W Y O U R C U S T O M E R ” F R A U D
Credit card application, loan
origination fraud results in…
…not only financial losses…
…but increased costs incurred
from time/resources used in
processing fake applications
CREDIT
APPLICATION

17. 17
COMMON TYPES OF FRAUD
“ B E F O R E Y O U K N O W Y O U R C U S T O M E R ”
Insurance industry: quote
manipulation
E-Commerce industry: multiple
online accounts
Gambling & gaming
industries: multiple online
accounts
APPLICATION

18. 18
MA N Y C R EATIVE WAYS TO C OMMIT ON LIN E FR A U D

19. 19
TOOLS OF THE TRADE
E V O L V I N G , M O R E P O T E N T I A L F O R D A M A G E
BOTNETS – automated
programs
EMULATORS &
SIMULATORS –
pretend to be other
devices
PROXY MASKING–
hide true locations
MACHINE LEARNING–
smarter, more human
like

20. 20
CYBERCRIMINALS COUNT ON…
ANONYMITY SCALE COMPLEXITY
FLEXIBILITY MOBILITY PERSISTENCE

21. SHORTCOMINGS OF
CURRENT FRAUD
PREVENTION
TECHNIQUES

22. 22
IP
ADDRESS
PERSON
FRAUD HAS TO
OCCUR FIRST
IT HAS TO BE
DISCOVERED
YOU NEED A
WAY TO BLOCK
IT

23. 23
SILOS CREATE BLIND SPOTS THAT ENABLE FRAUDSTERS
FRAUD
TEAM
PAYMENTS
TEAM
UNDERWRITIN
G
SECURITY
TEAM
YOUR
BUSINESS
YOUR
COMPETITOR
ANY
BUSINESS
FRAUDSTERS KNOW THIS. THEY EXPLOIT THIS.
PERSONAL
IDENTITY
DIGITAL
IDENTITY

24. 24
ARE YOUR
FRAUD PREVENTION EFFORTS
KILLING YOUR CUSTOMER EXPERIENCE?
 TOO MUCH FRICTION?
 SLOW PROCESSING TIMES?
 STOPPING GOOD CUSTOMERS?

25. 25
6 STEPS TO
NEXT GEN
FRAUD
PREVENTION
ADVANCED DEVICE
INTELLIGENCE
CONNECTING THE DOTS
HUMAN + MACHINE
A TEAM APPROACH
GUARDING THE
FRONT DOOR
MULTI-CHANNEL

26. 26
STEP 1
ADVANCED DEVICE
INTELLIGENCE

27. 27
BE PREPARED FOR ANY
TYPE OF ONLINE DEVICE
 No user friction
 No personal information
 Thwart evasion attempts and device
manipulation
 Re-recognize the same device
 Maintain a broad and deep device database
ACCURATE DEVICE RECOGNITION

28. 28
IDENTIFY DEVICE RISKS
 Context:
 Is device in a risky geolocation?
 Are there geo-location inconsistencies?
 Behavior:
 Is this a new device?
 Is transaction volume high?
 Is device being evasive?
 Characteristics:
 Are device attributes consistent?
 Are there device anomalies?
 Are there known risky attributes?

29. 29
DEVICE REPUTATION
 New device?
 Device have a past history of fraud or
abuse?
 Type of past fraud or abuse?
 Detailed & granular
 Associated with other devices with a bad
reputation?
 Vast database of fraud/abuse reports

30. 30
IOVATION DEVICE REPUTATION
FINANCIAL
FRAUD
MISCONDUCT CHEATING
IDENTITY
THEFT
POLICY
FRAUD
B2B
FINANCIAL
• Industry collaboration
• 4,000+ fraud analysts in the iovation network
• 55M+ confirmed fraud and abuse reports
placed
• 45 types of fraud & abuse reports in 6 major
categories

31. 31
IOVATION DEVICE REPUTATION
0%
5%
10%
15%
20%
25%
30%
35%
40%
RETAIL INDUSTRY CY 2017 (1.7M total)
0%
5%
10%
15%
20%
25%
30%
35%
FINANCIAL INDUSTRY CY 2017 (1.4M total)

32. 32
STEP 2
CONNECTING THE DOTS

33. 33
FRAUDSTERS RARELY USE JUST ONE DEVICE
 Identify relationships between
devices
 Don’t rely on personal identity
 If one device is involved in fraud,
then it’s likely other associated
devices will be risky too
 Effective at stopping credit
application fraud and other types
of new account fraud

34. 34
FRAUDSTERS RARELY USE JUST ONE DEVICE

35. 35
CHARIT
Y
RETAIL
A
BANK A BANK B
Stolen ID: New CC Synthetic ID: New CC
Stolen CC
Over time, iovation is able to determine that
these devices are associated with each other
CHARGE
BACK
For a while, no one realizes that there is a
problem even though the fraudsters have
successfully applied for new credit cards
THE POWER OF CONNECTIONS & REPUTATION

36. 36
STEP 3
 Both are needed for a comprehensive fraud
prevention strategy
 Separately they leave gaps

37. 37
AIMACHINE
LEARNING
DEEP
LEARNING
MACHINE LEARNING
Parse data
Learn from it
Make a prediction about
something in the world
Sometimes the prediction is
right. Sometimes not.
Rinse and repeat.

38. 38
Human
Machine
Learning
Detect obvious risk patterns ✓ ✓
Detect subtle risk patterns ✓
Stop specific, targeted threats ✓
Stop emerging threats ✓
Stop evolving threats ✓
H U MA N + MA C H IN E

39. 39
PREDICT
INSTEAD
OF REACTING
Save time.
Stop more fraud.
Better consumer experience.

40. 40
STEP 4
A TEAM APPROACH
Cybercriminals count on you not working together
 Collaborate. Collaborate.
Collaborate.
 Within your business
 With other businesses in your
industry
 With other businesses outside of
your industry
 Globally
 Stop repeat offenders

41. 41
STEP 5
Guard the front door
 Be proactive. Not reactive.
 Even if your business isn’t breached, the
chances are high that your customers have
been & they use the same username/password
 Fraud from ATO is preventable.
 Use frictionless device based
authentication
 Use Multi-factor authentication

42. 42
STEP 6
Multi-Channel Approach
 Plug one fraud hole and fraudsters just move to
another channel.
 Coordinate efforts between online, call center
channel, and on premise
 Form a comprehensive strategy
 Different fraud prevention tools prevent
different types of fraud
 Don’t rely on just one tool, process, or
technique

43. THE POWER OF
NEXT GEN FRAUD
PREVENTION

44. 44
BENEFITS
CATCH MORE
FRAUD
IMPROVE
OPERATIONAL
EFFICIENCIES
ADDRESS
COMPLIANCE
REQUIREMENTS
IMPROVE USER
ACCOUNT
SECURITY
IMPROVE
USER EXPERIENCE

45. 45
CUSTOMER CASE STUDY
 Credit application
fraud
 Long processing
times frustrated
customers and
reduced new loan
rates
 Device reputation
leveraged
 RESULTS:
 Significant reduction in
fraud loss
 Doubled loans approved
 Reduced customer
friction by 50%

46. 46
CUSTOMER CASE STUDY
 Large electronics relater
 Even with credit card validation,
reshipping scams were a
problem
 Difficulty in identifying related
orders
 RESULTS:
 Fraud catch tripled
 Reduced time to process
orders
 No increased friction

47. 47
10 BUYING CRITERIA
F O R N E X T - G E N F R A U D P R E V E N T I O N
Advanced Device
Recognition
1
Machine Learning
2
Human Insight
3
Online & Mobile
Support
4
Granular Device
Reputation
5
Active Industry
Participation
6
Device
Associations
7
Comprehensive
Device Risks
8
Flexible
Configuration
9
Service Reliability
10

48. Q&A