Self-Organizing Fuzzy and MLP Approaches to Detecting Fraudulent Financial Reporting

SOFT COMPUTING IN FINANCIAL ENGINEERING, R.A. Riberio, H.J. Zimmerman, R.R. Yager, & J. Kacpryzk, eds., New York, 1999

6 Pages Posted: 12 Aug 2008 Last revised: 20 Feb 2018

See all articles by Ehsan H. Feroz

Ehsan H. Feroz

University of Washington, Milgard School of Business-Accounting ; University of Illinois at Urbana-Champaign; Government of the United States of America - US GAO Advisory Council; University of Minnesota, Labovitz School of Business-Department of Accounting; University of Minnesota, Carlson School of Management-Department of Accounting; American Accounting Association

Taek M. Kwon

University of Minnesota - Twin Cities - Electrical and Computer Engineering

Date Written: August 10, 2008

Abstract

This paper predates the Sarbanes-Oxley Act of 2002 (Section 704: Study of Enforcement Actions) by three years. The purpose of this study is to compare a class of neural networks and fuzzy controller approaches, more specifically, a multi-layered perceptron (MLP) and a self-organizing fuzzy approach, in determining the efficacy of selected Statement of Auditing Standard No. 53 red flags in predicting the targets of the Securities and Exchange Commission's (SEC) investigations. The motivation for studying these two approaches is provided in part by our earlier work with conventional tools such as logit which generally leads to inferior prediction accuracy for such classification problems.

In direct comparison of the performance on the present SEC investigation problem, the MLP network outperformed both logit and fuzzy approaches. We believe that the performance of the fuzzy approach can be significantly improved if the center vectors are fine tuned using some adaptive algorithms. Our empirical results demonstrate that both MLP and fuzzy approaches can be powerful tools, especially in the pattern classification or detection problems, such as those related to fraudulent financial reporting.

Keywords: Fraudulent financial reporting, SEC, GAAP, SAS, Red flags, Sarbanes Oxley Act of 2002, Neural Networks, Fuzzy controller, Financial engineering, Soft Computing, Political Economy

JEL Classification: C45, D57, G18, G38, K22, L81, M41

Suggested Citation

Feroz, Ehsan H. and Kwon, Taek M., Self-Organizing Fuzzy and MLP Approaches to Detecting Fraudulent Financial Reporting (August 10, 2008). SOFT COMPUTING IN FINANCIAL ENGINEERING, R.A. Riberio, H.J. Zimmerman, R.R. Yager, & J. Kacpryzk, eds., New York, 1999, Available at SSRN: https://ssrn.com/abstract=1215302

Ehsan H. Feroz (Contact Author)

University of Washington, Milgard School of Business-Accounting ( email )

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Government of the United States of America - US GAO Advisory Council ( email )

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University of Minnesota, Labovitz School of Business-Department of Accounting ( email )

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University of Minnesota, Carlson School of Management-Department of Accounting ( email )

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American Accounting Association ( email )

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Taek M. Kwon

University of Minnesota - Twin Cities - Electrical and Computer Engineering ( email )

Duluth, MN 55812
United States

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