Self-organizing fuzzy and MLP approaches to detecting fraudulent financial reporting

Ehsan H. Feroz, Taek M Kwon

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

In the fields of accounting and auditing, detection of firms engaged in fraudulent financial reporting has become increasingly important, due to the increased frequency of such events and the attendant costs of litigation. Conventional statistical tools such as logit and probit have not been successful in detecting such firms. In this study, we employ seven redflags which are composed of four financial redflags and three turn over redflags in order to detect targets of the Securities and Exchange Commission's (SEC) investigation of fraudulent financial reporting. Two prominent nonlinear approaches, i.e. neural network and fuzzy sets, are applied to detection of SEC investigation targets and compared with the conventional statistical methods.

Original languageEnglish (US)
Pages279-285
Number of pages7
StatePublished - Dec 1 1996
EventProceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr - New York, NY, USA
Duration: Mar 24 1996Mar 26 1996

Other

OtherProceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr
CityNew York, NY, USA
Period3/24/963/26/96

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