Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/6691
Title: Neural networks: the panacea in fraud detection?
Authors: Agathocleous, Michalis 
Christodoulou, Chris 
Krambia-Kapardis, Maria 
Agathocleous, Michalis 
Christodoulou, Chris 
Keywords: Auditors
Fraud
Neural networks
Issue Date: 2010
Publisher: Emerald
Source: Managerial Auditing Journal, 2010, Volume 25, Issue 7, Pages 659-678
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.
URI: http://ktisis.cut.ac.cy/handle/10488/6691
ISSN: 02686902
DOI: 10.1108/02686901011061342
Rights: © Emerald Group Publishing Limited.
Appears in Collections:Άρθρα/Articles

Show full item record

SCOPUSTM   
Citations 20

8
checked on Jul 29, 2017

Page view(s) 50

24
Last Week
0
Last month
2
checked on Aug 22, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.