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Fraud and Risk Management:Universal Responsibility

Andrew Pease

29 April 2008

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Agenda

Fraud: Universal Problem/Universal Approach

Approach: Data and Awareness

Rules-Based Alerts

Advanced Analytics

Experiences

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Fraud–OneWorld?

What is Fraud? WAKE UP!!! Quiz time!

Ken LayChairman/CEO Enron

Sold 1.8 mil lion shares for more than $101.3million

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Finding Fraud is Like……finding a hay-coloured needle in a

haystack…

…scattered all over thefield…

…that doesn’t want tobe found…

…and may not even bethere!

Mission Impossible???

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Agenda

Fraud: Universal Problem/Universal Approach Approach: Data and Awareness

Rules-Based Alerts

Advanced Analytics

Experiences

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the iterative process of intelligently filtering large amounts of disparate, yet relevant 

data to uncover previously unknown patterns for highly focused investigation 

What are ‘Fraud Analytics’?

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Finding Fraud is Like……finding a hay-coloured needle in a

haystack…

…scattered all over thefield…

…that doesn’t want tobe found…

…and may not even bethere!

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Agenda

Fraud: Universal Problem/Universal Approach Solution: Data and Awareness

Rules-Based Approach

Advanced Analytics

Experiences

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Rules-Based Approach

IF-THEN logic

Centralise 80% of FieldBest Practices

Investigation Framework

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Discovery

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Agenda

Fraud: Universal Problem/Universal Approach Solution: Computers and Awareness

Rules-Based Approach

Advanced Analytics

Experiences

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Subsidized Amount

      I    n    c

    o    m    e

cluster4

cluster3

cluster1

cluster2

cluster5

Segmentation/Clustering

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Decision Tree

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SAS Fraud Detection

Differentiating Between Monitoring Approaches

Rules

Set up rulesto filter suspicious

transactions

Examples:Refund > 20.000

AND

Income < 30.000

Profiling

Build statisticalProfiles of

Entities andinteractions

Examples:Mean, standard

deviation, quartiles,

distributions

AdvancedAnalytics

Knowledge discovery

In databasesand machine

learning

Examples:Neural networks

Fuzzy logicGenetic algorithms

Hybrid

Combination ofall existing

approaches

Examples:Genetic algorithm and

Statistics plus

Neural network

Issues

• Detect known andunknown patterns offraudulent behavior

• Keep track with newpatterns Not exactlyknowing what to lookfor

Suitable forknown

patterns

Suitable forunknown

patterns

Suitable forcomplex

patterns

Best

Practice

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Agenda

Fraud: Universal Problem/Universal Approach Solution: Computers and Awareness

Rules-Based Approach

Advanced Analytics

Experiences

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KBC Internal Fraud

INPUTData 

THROUGHPUTAnalysis & mining 

OUTPUTApplication 

AFK

TMK

...

Known rules

KRD

New rules

Intelligence

Server

SAS EM

KBC LOA fraud risk

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Fraud Investigation at OLAF

 ACCESS Products

Enterprise Miner 

(incl. Text Miner)

ETL Business Intelligence Analytic Intelligence

SAS Data Set s

Cluster ing

Classi f icat ion

HTML

OCR

Reports

Tex t

Preprocessing

MS Word Oracle

Registration Management

Improved Case Follow-up

Access

Enterprise Guide

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Banksys Fraud Detection Approach

Data

Capture

OperationalSystems

Data

Transfer

FD&RServer

CMT

PC

SAS Fraud Detection

Engine

Data

Upload

FD&R Database

Alerts and InvestigationInformation

CardStop, ...

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AFK

TMK

...

Known rules

KRD

New rules

Intelligence

Server

SAS EM

KBC LOA fraud risk

D a t a

C a p t u r e

O p e r a t i o n a lS y s t e m s

D a t a

T r a n s f e r

F D & RS e r v e r

C M T

P C

S A S F r a u d D e t e c t i o nE n g i n e

D a t a

U p l o a d

F D & R D a t a b a s e

A l e r t s a n d I n v e s t i g a t i o nI n f o r m a t i o n

C a r d S t o p , . . .