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Atos, Atos and fish symbol, Atos Origin and fish symbol, Atos Consulting, and the fish itself are registered trademarks of Atos Origin SA. August 2006 © 2006 Atos Origin. Confidential information owned by Atos Origin, to be used by the recipient only. This document or any part of it, may not be reproduced, copied, circulated and/or distributed nor quoted without prior written approval from Atos Origin. Fraud Risk Management In Practice Olivier Caelen Business Analytics Competence Center 17 April 2012 ULB Séminaire Questions Actuelles d'Informatique

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Atos, Atos and fish symbol, Atos Origin and fish symbol, Atos Consulting, and the fish itself are registered trademarks of Atos Origin SA. August 2006

© 2006 Atos Origin. Confidential information owned by Atos Origin, to be used by the recipient only. This document or any part of it, may not be reproduced, copied,

circulated and/or distributed nor quoted without prior written approval from Atos Origin.

Fraud Risk Management In Practice

Olivier Caelen – Business Analytics Competence Center

17 April 2012

ULB Séminaire – Questions Actuelles d'Informatique

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 2

Agenda

ATOS Worldline

FRM Strategy

FRM Infrastructure

Staffing

Q&A

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 3

Agenda

ATOS Worldline

FRM Strategy

FRM Infrastructure

Staffing

Q&A

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 4

Subsidiary of

an IT Company

Specialized in critical

electronic transaction

processing

End to end

service provider

Atos Worldline is…

Hi-Tech Transactional Services

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 5

65% 24% 11%

Hi-Tech Transactional Services

Electronic PaymentseCS

Customer, Citizen &

e-Community Services

Financial Markets

Global Turnover = 844m€

4800 employees

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 6

Electronic Payments

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 7

ATOS Worldline in Electronic Payments

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 8

Agenda

ATOS Worldline

FRM Strategy

FRM Infrastructure

Staffing

Q&A

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 9

FRM Strategy

Awareness

Technical infrastructure

EMV

3D-Secure

Analysis

Transaction scoring

Case investigation

(Mass-) blocking

Cardstop

Chargeback

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 10

FRM Strategy In Practice

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Breakdown by Fraud Type (Credit - Issuing)

INTERNET

MOTO

CF

NRI

LST/STL

Counterfeit Lost/Stolen Internet

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 12

FRM Detection Strategy In Practice

Skimming Detection

Skimming Abuse

2002

Skimming Abuse

2004

Abuse

2007

Skimming

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 13

Agenda

ATOS Worldline

FRM Strategy

FRM Infrastructure

Staffing

Q&A

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 14

FRM Infrastructure

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 15

Daily Average: 170.000 people

use their Credit Card.

Daily Average: 17 Fraud Cases.

Inte

llig

en

t

Filt

er

Expert Driven Fraud Detection

Translate Domain

Expertise into Rules

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 16

Expert Driven Fraud Detection - Approach

N_Trans

Tot_Amt

x

x

x x

x

x

x

x

xoo

o

oo

o

ooo

oo

o

o

o

o

o

o

o

o

o

o

o

o

50 1000

1000

2000

3000

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 17

Expert Driven Fraud Detection - Approach

» If N_Trans > 80 and Tot_Amt > 2000 then ‘x’

N_Trans

Tot_Amt

x

x

x x

x

x

x

x

xoo

o

oo

o

ooo

oo

o

o

o

o

o

o

o

o

o

o

o

o

50 1000

1000

2000

3000

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 18

Expert Driven Fraud Detection – Advantages

and Limitations

» Advantages

› Fast development

› Easy to understand

› Explain why an alert was generated

› Exploit Domain Expert knowledge

» Limitations

› Ask 7 experts, get 7 opinions

› Hard boundaries

• From Tot_Amt = 10 to Tot_Amt = 1.990 changes nothing

• From Tot_Amt = 1.990 to Tot_Amt = 2.010 changes everything

› Space is ‘boxed’

› Humans have difficulties thinking in more than 3 dimensions

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 19

Expert Driven Fraud Detection –

Limitations, less easy

N_Trans

Tot_Amt

x

x

x x

x

x

x

x

xoo

x

ox

o

oox

oo

o

o

o

o

o

o

o

o

x

o

o

o

50 1000

1000

2000

3000

x

xx

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 20

Expert Driven Fraud Detection –

Limitations, less easy

x

x

x

N_Trans

Tot_Amt

x

x

x x

x

x

x

x

xoo

o

o

oox

oo

o

o

o

o

o

o

o

o

o

o

o

50 1000

1000

2000

3000

x

xx

If (N_Trans > 80 and Tot_Amt > 2000) or (N_Trans < 40 and Tot_Amt < 1500) then ‘x’

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 21

Expert Driven Fraud Detection –

Limitations, less easy

N_Trans

Tot_Amt

x

x

x x

x

x

x

x

xoo

x

ox

o

oox

oo

o

o

o

o

o

o

o

o

x

o

o

o

50 1000

1000

2000

3000

x

xx

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 22

Expert Driven Fraud Detection –

Limitations, hard

N_Trans

Tot_Amt

x

x

x

x

x

x

x

o

o

o

o

o

o

o

o

o

o

o

o

o

50 1000

1000

2000

3000

x

x

x

x x

xx x

xx

x

x

x x

x

x

xx

x

x

oo

o

o

o

oo

o

o

o

o

o

oo

o o

o

x

x

x

xo

o

o

o

o

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 23

Expert Driven Fraud Detection –

Limitations, hardest

» Think of the previous shape in 6 dimensions.

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 24

The Fraud Detection Problem

» Fraud behaviour is complex

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 25

Data Driven Fraud Detection

Daily Average: 170.000 people

use their Credit Card.

Daily Average: 17 Fraud Cases.

Inte

llig

en

t

Filt

er

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 26

Data Driven Fraud Detection – Data Mining

» ‘Data’› Large volumes› Complex, high dimensional

» ‘Mining’› Actionable information› Obscure, difficult to uncover

Extracting actionable information from large volumes of data

» In our case: Use historical information as examples to build a model that distinguishes between fraud and genuine examples. Score new information with the model to estimate the probability of fraud.

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 27

Data Driven Fraud Detection – Advantages

» Advantages› Optimal model based on all available cases

› Robust, i.e. their response is smoother than rules

0.0 5.00

0.5

0.0

-5.0

1.0

0.0 5.00

0.5

Rules

0.0

-5.0

1.0

Neural Network

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 28

Data Driven Fraud Detection – Advantages

» Can model complex shapes

» Scale naturally to high dimensional problems

Examples Model

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 29

Data Driven Fraud Detection – Limitations

» Limitations› Need examples

› Development needs specialised

• Resources (data management, data mining, domain knowledge, …)

• Software

• Time

› For some modelling technologies, no explanation why the alert was generated is

available

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 30

Conclusion

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 31

Agenda

ATOS Worldline

FRM Strategy

FRM Infrastructure

Staffing

Q&A

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 32

Staffing

» The Business Analytics Competence Center

» A small team of highly skilled professionals in

› Data Management

› Data Mining

› Reporting

» Our Mission: Turn company data into value for us and for our customers

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 33

Agenda

ATOS Worldline

FRM Strategy

FRM Infrastructure

Staffing

Q&A

HIGH-TECH TRANSACTIONAL SERVICESFRM In Practice – 34

Thank you for your attention