1. Easy Solutions is a leading global provider of electronic fraud prevention for financial institutions and enterprise customers, protecting over 75 million users and monitoring over 22 billion online connections in the last 12 months. 2. Alejandro Correa Bahnsen is a data scientist at Easy Solutions who has over 8 years of experience in data science and works on fraud detection and prevention. 3. Fraud analytics uses machine learning and artificial intelligence techniques to analyze customer transaction data and detect patterns that can predict fraudulent transactions from legitimate ones.

1. Easy Solutions

2. About us
Industry recognitionA leading global provider of electronic fraud
prevention for financial institutions and enterprise
customers
280+ customers
In 26 countries
75 million
Users protected
22+ billion
Online connections monitored in
last 12 months
2

3. Some of our Customers
3

4. Our Approach:Total Fraud Protection®
4

5. Fraud Analytics
Alejandro Correa Bahnsen, PhD
Data Scientist

6. About me
• PhD in Machine Learning at Luxembourg University
• Data Scientist at Easy Solutions
• Worked for +8 years as a data scientist at GE Money, Scotiabank
and SIX Financial Services
• Bachelor and Master in Industrial Engineering
• Organizer of Data Science Luxembourg and recently of Big Data
Science Bogota
6

7. ~1Billion USD
~171Millions USD
~3Billions USD
Does fraud affect me?
7

8. € -
€ 100
€ 200
€ 300
€ 400
€ 500
€ 600
€ 700
€ 800
2007 2008 2009 2010 2011 2012
Europe fraud evolution
Card not present (Internet) transactions
8

9. $-
$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
$4,000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
US fraud evolution
Card not present (Internet) transactions
9

10. 1.10%
1.30%
1.10%
0.90% 0.88% 0.87%
0.09% 0.08% 0.08% 0.06% 0.05% 0.05%
2006 2007 2008 2009 2010 2011
Card Present vs. Card Not Present Fraud Rates
Card Not Present Card Present
23.3
26.8
30.0
33.3
35.0
2009 2010 2011 2012 2013
US Online Banking
Billions of Transactions
1.2
3.0
5.6
9.4
14.0
2009 2010 2011 2012 2013
US Mobile Banking
Billions of Transactions
10

11. There is a need for
better fraud
detection strategies
11

12. 12

13. BigData?
13

14. “War is ninety percent information”
• Napoleon Bonaparte
14

15. 15

16. 16

17. Big data (Data Science) is like teenage sex:
everyone talks about it,
nobody really knows how to do it,
everyone thinks everyone else is doing it,
so everyone claims they are doing it...
17

18. 18

19. BigData Analytics
19

20. BigData Analytics is the
use of methods and
tools of Machine
Learning and Artificial
Intelligence with the
objective making data-
driven decisions
20

21. Fraud detection
and prevention
21

22. Estimate the probability of a transaction being fraud based on analyzing
customer patterns and recent fraudulent behavior
Issues when constructing a fraud detection system:
• Skewness of the data
• Cost-sensitivity
• Short time response of the system
• Dimensionality of the search space
• Feature preprocessing
• Model selection
22
Credit card fraud detection

23. Network
Fraud??
23

24. • Larger European card processing
company
• 2012 & 2013 card present
transactions
• 20MM Transactions
• 40,000 Frauds
• 0.467% Fraud rate
• ~ 2MM EUR lost due to fraud on
test dataset
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
Test
Train
Data

25. • “Purpose is to use facts and rules, taken from the knowledge
of many human experts, to help make decisions.”
• Example of rules
• More than 4 ATM transactions in one hour?
• More than 2 transactions in 5 minutes?
• Magnetic stripe transaction then internet transaction?
25
If-Then rules (Expert rules)

26. 1.04%
31%
17%
22%
Miss-cla Recall Precision F1-Score
26
If-Then rules (Expert rules)

27. Credit card fraud detection is a cost-sensitive problem. As the cost due to a
false positive is different than the cost of a false negative.
• False positives: When predicting a transaction as fraudulent, when in
fact it is not a fraud, there is an administrative cost that is incurred by
the financial institution.
• False negatives: Failing to detect a fraud, the amount of that transaction
is lost.
Moreover, it is not enough to assume a constant cost difference between
false positives and false negatives, as the amount of the transactions varies
quite significantly.
27
Financial evaluation

28. Cost matrix
𝐶𝑜𝑠𝑡 𝑓 𝑆 =
𝑖=1
𝑁
𝑦𝑖 𝑐𝑖 𝐶 𝑇𝑃 𝑖
+ 1 − 𝑐𝑖 𝐶 𝐹𝑁 𝑖
+ 1 − 𝑦𝑖 𝑐𝑖 𝐶 𝐹𝑃 𝑖
+ 1 − 𝑐𝑖 𝐶 𝑇𝑁 𝑖
28
Actual Positive
𝒚𝒊 = 𝟏
Actual Negative
𝒚𝒊 = 𝟎
Predicted Positive
𝒄𝒊 = 𝟏
𝐶 𝑇𝑃 𝑖
= 𝐶 𝑎 𝐶 𝐹𝑃 𝑖
= 𝐶 𝑎
Predicted Negative
𝒄𝒊 = 𝟎
𝐶 𝐹𝑁 𝑖
= 𝐴𝑚𝑡𝑖 𝐶 𝑇𝑁 𝑖
= 0
Financial evaluation

29. 1.24 €
1.94 €
Cost Total Losses
1.04%
31%
17%
22%
Miss-cla Recall Precision F1-Score
29
If-Then rules (Expert rules)

30. Fraud Analytics
30

31. Raw features
31
Attribute name Description
Transaction ID Transaction identification number
Time Date and time of the transaction
Account number Identification number of the customer
Card number Identification of the credit card
Transaction type ie. Internet, ATM, POS, ...
Entry mode ie. Chip and pin, magnetic stripe, ...
Amount Amount of the transaction in Euros
Merchant code Identification of the merchant type
Merchant group Merchant group identification
Country Country of trx
Country 2 Country of residence
Type of card ie. Visa debit, Mastercard, American Express...
Gender Gender of the card holder
Age Card holder age
Bank Issuer bank of the card
Features

32. Transaction aggregation strategy
32
Raw Features
TrxId Time Type Country Amt
1 1/1 18:20 POS Lux 250
2 1/1 20:35 POS Lux 400
3 1/1 22:30 ATM Lux 250
4 2/1 00:50 POS Ger 50
5 2/1 19:18 POS Ger 100
6 2/1 23:45 POS Ger 150
7 3/1 06:00 POS Lux 10
Aggregated Features
No Trx
last 24h
Amt last
24h
No Trx
last 24h
same
type and
country
Amt last
24h same
type and
country
0 0 0 0
1 250 1 250
2 650 0 0
3 900 0 0
3 700 1 50
2 150 2 150
3 400 0 0
Features

33. When is a customer expected to
make a new transaction?
Considering a von Mises
distribution with a period of 24
hours such that
𝑃(𝑡𝑖𝑚𝑒) ~ 𝑣𝑜𝑛𝑚𝑖𝑠𝑒𝑠 𝜇, 𝜎
=
𝑒 𝜎𝑐𝑜𝑠(𝑡𝑖𝑚𝑒−𝜇)
2𝜋𝐼0 𝜎
where 𝝁 is the mean, 𝝈 is the standard
deviation, and 𝑰 𝟎 is the Bessel function
33
Periodic features

34. 34
Periodic features

35. Fraud Analytics is the use of statistical
and mathematical techniques (Machine
Learning) to discover patterns in data in
order to make predictions
Fraud Analytics

36. Amountofthetransaction
Number of transactions last day
Normal Transaction
Fraud
36

37. 37
Amountofthetransaction
Number of transactions last day
Normal Transaction
Fraud

38. 38
Amount of the transaction
Normal Transaction
Fraud
Number of transactions last dayNumber of ATM transactions
last week

39. Fraud Analytics
Algorithms
Fuzzy Rules
Neural Nets
Naive Bayes
Random Forests
Cost-Sensitive Random Patches
Decision Trees
39

40. 0%
20%
40%
60%
80%
100%
Expert Rules Fuzzy Rules Neural Nets Naïve Bayes Random
Forests
CS Random
Patches
% Savings % Frauds
40

41. • Fraud Analytics (ML) models are significantly
better than expert rules
• Models should be evaluated taking into
account real financial costs of the application
• Algorithms should be developed to
incorporate those financial costs
Conclusions
41

42. Questions?
Alejandro Correa Bahnsen, PhD
Data Scientist
acorrea@Easysol.net
42