A RESEARCH TAXONOMY:THE APPLICATION OF DATA MINING TO FRAUD DETECTION

Glen L. Gray California State University at Northridge

Roger Debreceny University of Hawai‘i at Mānoa

8th Biennial Symposium onInformation Integrity and Information Systems AssuranceOctober, 2013

Introduction

• Observation• The application of data mining to fraud detection during

financial audits is at an early stage of development and researchers take a scatter-shot approach

• Objectives of our study• Explore the application of data mining techniques to fraud

detection• Develop a taxonomy to support and guide future research

Data Examination Tools

Data

Analysis

Data Mining

Data

Extraction &

Query

Software sophistication

Pre

dic

tive

Po

wer

GRAB THELOW HANGINGFRUIT ..

Return on investment in data mining

Spread investment in datamining over many clients

Return on investment in data mining

Spread investment in datamining for one client over

many possible fraud objects

LOOKING FOR THE SWEET SPOT .. Where can we leverage value from data mining in fraud detection?

Looking for the sweet spot!

Scheme Scheme Scheme Scheme Scheme

Fraud

Fraud

Fraud

Fraud

Fraud

Fraud

Looking for the sweet spot!

Scheme Scheme Scheme Scheme Scheme

Fraud

Fraud

Fraud

Fraud

Fraud

Fraud

Looking for the sweet spot!

Scheme Scheme Scheme Scheme Scheme

Fraud

Fraud

Fraud

Fraud

Fraud

Fraud

Fraud Class by Evidence Scheme

Gao, L., and R. P. Srivastava. 2011. The Decomposition of Management Fraud Schemes: Analyses and Implications. Indian Accounting Review 15 (1):1-23.

Audit Specific Data Mining Scoring Scheme

Source TargetSignals Data Types Semantics

Scoring Elements

Fraud Class, Evidence Scheme and Data Mining

Fak

e D

ocu

men

ts

Co

llu

sio

n w

ith

th

ird

Par

ties

Alt

ered

Do

cum

ents

Hid

den

Do

cum

ents

Cli

ent

Mis

rep

rese

nta

tio

ns

Fak

e P

rod

uct

s

Rel

ated

Par

ties

Sp

read

ing

of

Fra

ud

ule

nt

Item

s

amo

ng

Acc

ou

nts

Rev

ersa

l A

cco

un

tin

g E

nti

ties

Fictitious Revenue

Premature Revenue Recognition

Overstated Assets/Understated Liabilities

Nil Low

Fictitious Assets LowMedium

Other Measures to Overstate Revenue

Medium

High

Overvalued Assets/Equity

High

Omitted Disclosure

Frequency Data MiningApplicability

FUTURE RESEARCH

Themes in Data Mining

• Mining External Information as Part of the Planning Phase

• Mining Client Non-financial performance data • Analysis of Journal Entries • Mining Accounting Information Systems • Email and other textual sources