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A fuzzy neural network for assessing the risk of fraudulent financial reporting

Jerry W. Lin (Department of Accounting, University of Minnesota Duluth, Duluth, Minnesota, USA)
Mark I. Hwang (Department of Business Information Systems, Central Michigan University, Mt Pleasant, Michigan, USA)
Jack D. Becker (Business Computer Information Systems Department, University of North Texas, Denton, Texas, USA)

Managerial Auditing Journal

ISSN: 0268-6902

Article publication date: 1 November 2003



While financial reporting fraud has become more prevalent and costly in recent years, fraud detection has been badly lagging. Several recent studies have examined the feasibility of various computer techniques in business and industrial applications. The purpose of this study is to evaluate the utility of an integrated fuzzy neural network (FNN) for fraud detection. The FNN developed in this research outperformed most statistical models and artificial neural networks (ANN) reported in prior studies. Its performance also compared favorably with a baseline Logit model, especially in the prediction of fraud cases.



Lin, J.W., Hwang, M.I. and Becker, J.D. (2003), "A fuzzy neural network for assessing the risk of fraudulent financial reporting", Managerial Auditing Journal, Vol. 18 No. 8, pp. 657-665.




Copyright © 2003, MCB UP Limited

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