Self-Organizing Fuzzy and MLP Approaches to Detecting Fraudulent Financial Reporting
Ehsan H. Feroz
University of Washington, Tacoma-Milgard School of Business; Vernon Zimmerman Center, University of Illinois; US Government Accountability Office
Taek M. Kwon
University of Minnesota - Twin Cities - Electrical and Computer Engineering
August 10, 2008
SOFT COMPUTING IN FINANCIAL ENGINEERING, R.A. Riberio, H.J. Zimmerman, R.R. Yager, & J. Kacpryzk, eds., New York, 1999
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.
Number of Pages in PDF File: 6
Keywords: Fraudulent financial reporting, SEC, GAAP, SAS, Red flags, Neural Networks, Fuzzy controller, Financial engineering, Soft Computing
JEL Classification: C45, D57, G18, G38, K22, L81, M41
Date posted: August 12, 2008 ; Last revised: July 26, 2014
© 2016 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollobot1 in 0.235 seconds