Machine Learning represents an exciting way forward for financial institutions (FIs) looking for more effective ways to predict fraud - especially new types of fraud that have not been seen before. While most FI's recognize the promise that ML analytics will have in helping them root out a variety of fraud types, many of them remain in the early stages of this journey. iovation recently partnered with research firm Aite Group to better gauge how FI's are currently employing ML in their fraud prevention strategy and their plans to adopt ML technologies in the coming years. In this webinar, Julie Conroy, Research Director for Aite alongside Eddie Glenn, product marketing manager for iovation, will explore some of the key findings from this report and based on the survey findings, will provide guidance to FIs of all sizes for implementing a successful ML strategy.

1. 1
MACHINE
LEARNING: FRAUD
IS NOW A
COMPETITIVE ISSUE
N O V E M B E R | 2 0 1 7

2. 2
EDDIE GLENN
P R O D U C T M A R K E T I N G M A N A G E R , I O V A T I O N
JULIE CONROY
R E S E A R C H D I R E C T O R / A I T E G R O U P

3. AGENDA
3
 INDUSTRY TRENDS
 AITE’S SURVEY AND
FINDINGS
 USING MACHINE LEARNING
TO FIGHT FRAUD

4. 4
Source:
Informationisbeautifu
l.net

5. 5
$30 to $99
40%
$100 to $199
15%
$200 or more
45%
Participating FIs by Asset Size
(In US$ billions; N=20)
METHODOLOGY
Source: Aite Group interviews with 28 executives at 20 large North
American FIs, August and September 2017

6. 6
$3.2 $3.3
$4.0 $4.4
$5.5 $5.9
$1.4 $1.6
$1.9
$2.2
$2.5
$2.8
$0.6
$0.7
$0.8
$0.8
$0.9
$1.0
2015 2016 e2017 e2018 e2019 e2020
U.S. ATO, CNP, and Application Fraud Growth, 2015 to e2020
(In US$ Billions)
ATO fraud
Application
fraud
CNP fraud
COUNTERFEIT IS DECLINING
W H I L E A T O , A P P L I C A T I O N F R A U D , A N D C N P I S R I S I N G
Source: Aite Group, 2017

7. 7
18
10
5
4
3
1
Retail ATO Application
fraud
Wholesale ATO Faster
payments
Card-not-
present
fraud
Check fraud
Q. What types of fraud represent your biggest priority for investment over
the next couple years? (N=20)
KEY PAIN POINTS
R E T A I L A T O A N D A P P L I C A T I O N F R A U D
Source: Aite Group interviews with 28 executives at 20 large North
American FIs, August and September 2017

8. Fraudsters

9. 9
THE CHANGING NOTION OF IDENTITY
F R O M F A C E - T O - F A C E , T O P E R S O N A L L Y I D E N T I F I A B L E I N F O R M A T I O N T O D I G I T A L I D E N T I T Y

10. 10
MACHINE LEARNING
B U Z Z W O R D R E A L I Z I N G I T S P O T E N T I A L

11. MACHINE LEARNING TURNS THE DATA LAKE INTO ACTIONABLE
INTELLIGENCE

12. 12
THE TERM IS NOT ONE -SIZE-FITS ALL
The ML analytics are an embedded part of a point solution,
often used to enhance scoring algorithms.
Embedded
analytics
Enabling
platform
Analytic
toolkit
The analytics toolkit is provided to data scientists at banks and
merchants; they use it to construct their own analytics models.
The ML platform is an analytics engine that enables businesses
to deploy ML models at scale, without the need for large in-
house data science functions.

13. 13
Very high—this is
a key area of
investment
65%
Moderate—it’s on
the roadmap, but
other solutions will
take priority
35%
Q. What level of priority do ML fraud analytic solutions have for
investment at your FI? (N=20)
FIS ARE PRIORITIZING INVESTMENT IN ML
Source: Aite Group interviews with 28 executives at 20 large North
American FIs, August and September 2017

14. 14
Yes, in production
40%
Yes, in POC
10%
No, no plans
30%
No, on the 1- to 2-
year roadmap
20%
Q. Are you using an ML-enabling platform today? (N=20)
INVESTMENTS ARE UNDERWAY
80% of FIs interviewed
have in-house data
science teams dedicated
to fraud
Use cases range from
check fraud to card fraud
to digital banking
Source: Aite Group interviews with 28 executives at 20 large North
American FIs, August and September 2017

15. v

16. 16
All supervised
25%
Primarily
supervised
25%
Supervised and
unsupervised
15%
No ML yet
35%
Use of Supervised and Unsupervised Modeling Techniques Among
Respondents (N=20)
ML ISN’T JUST UNSUPERVISED ANALYTICS
Source: Aite Group interviews with 28 executives at 20 large North
American FIs, August and September 2017

17. 17
100%
55%
75%
90%
90%
40%
25%
10%
10%
5%
We use rules to supplement our analytics
We use ML-modeling techniques to help identify
new rule sets
We are trying to move away from using rules
Rules will always be needed to some degree
Trained fraud experts are required to analyze the
output and optimize the efficiency of ML models
Q. Please indicate the extent to which you agree with each of the following
statements. (N=20)
Agree Disagree No opinion
STILL A ROLE FOR RULES AND PEOPLE
Source: Aite Group interviews with 28 executives at 20 large North
American FIs, August and September 2017

18. 18
Yes, in production
today
10%
In process or
implementing
30%
On the 1- to 2-
year roadmap
20%
No
40%
Q. Do you use ML analytics to help orchestrate authentication? (N=20)
OR C H ESTR ATION OF A U TH EN TIC ATION IS A FOC U S
Source: Aite Group interviews with 28 executives at 20 large North
American FIs, August and September 2017

19. REGULATORS: THE CLOUD ON THE HORIZON

20. 20
Cross-channel
25%
Neither
35%
Cross-channel
and cross-product
40%
Use of Cross-Channel and Cross-Product Data (N=20)
THE DATA JOURNEY IS ONGOING
Structured only
55%
Structured and
unstructured
45%
Use of Structured vs. Unstructured Data (N=20)
Source: Aite Group interviews with 28 executives at 20 large North
American FIs, August and September 2017

21. 21
FR A U D MITIGATION IS N OW A C OMPETITIVE ISSU E

22. MACHINE LEARNING
AND
DEVICE
INTELLIGENCE

23. 23
DEVICE
INTELLIGENCEA proxy for real people
Device risk characteristics
Device reputation
Relationships with other devices

24. 24
Stop targeted attacks
Immediately address
newly identified threats
Implement business &
peace of mind policies
Highly configurable
 WAIT for fraud to occur
 DISCOVER the pattern
(if one exists)
 REACT by writing a rule to
prevent future fraud
TRADITIONAL FRAUD PREVENTION
M A N U A L , R U L E S - B A S E D
Reactive
Gets outdated
Local visibility only
Obvious patterns only

25. 25
30B+
TRANSACTIONS
4B+
DEVICES
1M+
COMBOS OF
ATTRIBUTES
100+
DEVICE &
TRANSACTION
ATTRIBUTES
45M+
FRAUD
REPORTS
BIG DATA,
MACHINE LEARNING,
AND DEVICE INTELLIGENCE

26. 26
ML TRAINED BY INDUSTRY PROFESSIONALS
I O V A T I O N H A S A N E T W O R K O F 4 , 0 0 0 F R A U D A N A L Y S T S
 Fraud report submitted
when fraud is confirmed by
an analyst
 Detailed types of fraud:
credit card fraud, loan
default, 1st party
application, 3rd party
application, ID theft,
synthetic ID, etc
 Data used to train our
machine learning models
 45M fraud reports placed
 45 different types of fraud tracked
 13+ years of data
 Crosses businesses and industries

27. 27
WHAT IF YOU COULD
PREDICT THE
OUTCOME OF
ANY ONLINE
TRANSACTION?
Will it be fraudulent?
Or is this your next best customer?

28. 28
IDENTIFYING
TRANSACTION
RISKS
BEHAVIORAL RISKS
e.g. Transaction velocity
DEVICE ATTRIBUTE RISKS
e.g. screen resolution, jailbroken
GEOLOCATION RISKS
e.g. specific location,
location mismatch
KNOWN RISKS
e.g. known fraud, associated to other
devices known for fraud
Transaction risks from:
Device + Account + Business

29. 29
RULES BASED:
Stop targeted attacks
Immediately address
newly identified threats
Implement business &
peace of mind policies
Highly configurable
A C OMPR EH EN SIVE FR A U D PR EVEN TION
STR ATEGY
Reactive
Gets outdated
Local visibility only
Obvious patterns only
ML:
Broad protection
Adaptive & predictive
Responds to changing
patterns
Global perspective
Subtle patterns
C O M B I N I N G M L W I T H T R A D I T I O N A L F R A U D P R E V E N T I O N

30. 30
 Offer special incentives, promotions to win new
business
 Offer special rewards for existing customers
 Reduce/soften other risk mitigation protocols (e.g.
expedite order processing)
IMPROVE THE CUSTOMER EXPERIENCE
W H A T I F Y O U C O U L D P R E D I C T A T R A N S A C T I O N W I L L B E G O O D ?

31. 31
BENEFITS
FROM
MACHINE
LEARNING
STOP MORE
FRAUD
• LEVERAGE global assessment
• DISCOVER subtle risk trends
REDUCE
REVIEW
QUEUES
• STOP risky transactions
• PRIORITIZE review queue
• REDUCE costs of manual reviews
BETTER USER
EXPERIENCE
• MINIMIZE friction for good customers
• REWARD great customers
• EXPEDITE order processing

32. AGENDA
32
 The machine learning revolution
has arrived
 Machine learning converts big
data into actionable intelligence
 Combine machine learning with
human intelligence for a
comprehensive fraud prevention
strategy
SUMMARY

33. 33
G O T O W W W . I O V A T I O N . C O M / R E S O U R C E S
RESOURCES
AITE REPORT: MACHINE LEARNING:
FRAUD IS NOW A COMPETITIVE ISSUE
The full report on how financial
institutions are using machine learning
to fight fraud.
SureScore Data Sheet
iovation SureScore uses machine
learning to stop fraud and detect good
customers.

34. Q&A

35. 35
Thank you.
Julie Conroy | Research Director
jconroy@aitegroup.com
Aite Group is a global research and advisory firm
delivering comprehensive, actionable advice on
business, technology, and regulatory issues and their
impact on the financial services industry. With expertise
in banking, payments, insurance, wealth management,
and the capital markets, we guide financial institutions,
technology providers, and consulting firms worldwide.
We partner with our clients, revealing their blind spots
and delivering insights to make their businesses smarter
and stronger.
Visit us on the Web and connect with us on Twitter and
LinkedIn.