Neural Network Detection of Management Fraud Using Published Financial Data

Posted: 20 Dec 1996

See all articles by Kurt Fanning

Kurt Fanning

Central Missouri State University

Kenneth O. Cogger

University of Kansas

Date Written: August 1996

Abstract

This paper uses Artificial Neural Networks to develop a model for detecting management fraud. Although similar to the more widely investigated area of bankruptcy prediction, research has been minimal. To increase the body of knowledge on this subject, we offer an in-depth examination of important publicly available predictors of fraudulent financial statements. We test the value of these suggested variables for detection of fraudulent financial statements within a matched pairs sample. We use a self organizing Artificial Neural Network (ANN) AutoNet in conjunction with standard statistical tools to investigate the usefulness of these publicly available predictors. Our study results in a model with a high probability of detecting fraudulent financial statements on one sample. The study reinforces the validity and efficiency of AutoNet as a research tool and provides additional empirical evidence regarding the merits of suggested red flags for fraudulent financial statements.

JEL Classification: M40, M49

Suggested Citation

Fanning, Kurt and Cogger, Kenneth O., Neural Network Detection of Management Fraud Using Published Financial Data (August 1996). Available at SSRN: https://ssrn.com/abstract=2846

Kurt Fanning (Contact Author)

Central Missouri State University ( email )

Warrensburg, MO 64093-5070
United States
816-543-8555 (Phone)
816-543-8885 (Fax)

Kenneth O. Cogger

University of Kansas ( email )

1415
Lawrence, KS 66045
United States

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