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Neural networks: the panacea in fraud detection?

Neural networks: the panacea in fraud detection? Purpose – The purpose of the paper is to test the use of artificial neural networks (ANNs) as a tool in fraud detection. Design/methodology/approach – Following a review of the relevant literature on fraud detection by auditors, the authors developed a questionnaire which they distributed to auditors attending a fraud detection seminar. The questionnaire was then used to develop seven ANNs to test the usage of these models in fraud detection. Findings – Utilizing exogenous and endogenous factors as input variables to ANNs and in developing seven different models, an average of 90 per cent accuracy was found in the fraud detection prediction model. It has, therefore, been demonstrated that ANNs can be used by auditors to identify fraud‐prone companies. Originality/value – Whilst previous researchers have looked at empirical predictors of fraud, fraud risk assessment methods and mechanically fraud risk assessment methods, no other research has combined both exogenous and endogenous factors in developing ANNs to be used in fraud detection. Thus, auditors can use ANNs as complementary to other techniques at the planning stage of their audit to predict if a particular audit client is likely to have been victimized by a fraudster. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Managerial Auditing Journal Emerald Publishing

Neural networks: the panacea in fraud detection?

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References (47)

Publisher
Emerald Publishing
Copyright
Copyright © 2010 Emerald Group Publishing Limited. All rights reserved.
ISSN
0268-6902
DOI
10.1108/02686901011061342
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of the paper is to test the use of artificial neural networks (ANNs) as a tool in fraud detection. Design/methodology/approach – Following a review of the relevant literature on fraud detection by auditors, the authors developed a questionnaire which they distributed to auditors attending a fraud detection seminar. The questionnaire was then used to develop seven ANNs to test the usage of these models in fraud detection. Findings – Utilizing exogenous and endogenous factors as input variables to ANNs and in developing seven different models, an average of 90 per cent accuracy was found in the fraud detection prediction model. It has, therefore, been demonstrated that ANNs can be used by auditors to identify fraud‐prone companies. Originality/value – Whilst previous researchers have looked at empirical predictors of fraud, fraud risk assessment methods and mechanically fraud risk assessment methods, no other research has combined both exogenous and endogenous factors in developing ANNs to be used in fraud detection. Thus, auditors can use ANNs as complementary to other techniques at the planning stage of their audit to predict if a particular audit client is likely to have been victimized by a fraudster.

Journal

Managerial Auditing Journal – Emerald Publishing

Published: Jul 27, 2010

Keywords: Neural nets; Fraud; Auditors; Financial reporting

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