Detection of management fraud: a neural network approach

Author

Fanning, Kurt ; Cogge, Kenneth O. ; Srivastava, Rajendra

Author_Institution

Sch. of Bus., State Univ. of New York, New Paltz, NY, USA

fYear

1995

fDate

20-23 Feb 1995

Firstpage

220

Lastpage

223

Abstract

The detection of management fraud is an important issue facing the auditing profession. A major contributor to this issue is the Loebbecke and Willingham (1989) conceptual model for the detection of management fraud. A cascaded Logit approach using the Loebbecke and Willingham model was developed (Bell et al., 1993). The present study offers an alternative approach using artificial neural networks (ANNs). This paper develops a successful discriminator of management fraud using both the generalized adaptive neural network architectures (GANNA) and the adaptive logic network (ALN) approaches to designing neural networks. The discriminant functions can distinguish between fraudulent and non-fraudulent companies with superior accuracy to the cascaded Logit results

Keywords

auditing; financial data processing; fraud; management; neural net architecture; GANNA; adaptive logic network; artificial neural networks; auditing profession; cascaded Logit approach; cascaded Logit results; conceptual model; discriminant functions; fraudulent companies; generalized adaptive neural network architectures; management fraud detection; neural network approach; Adaptive systems; Artificial neural networks; Crisis management; Environmental management; Logic design; Neural networks; Risk analysis; Risk management; Synthetic aperture sonar; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence for Applications, 1995. Proceedings., 11th Conference on

Conference_Location

Los Angeles, CA

Print_ISBN

0-8186-7070-3

Type

conf

DOI

10.1109/CAIA.1995.378820

Filename

378820