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

Jerry W. Lin, Mark I. Hwang, Jack D. Becker

Research output: Contribution to journalArticlepeer-review

118 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)657-665
Number of pages9
JournalManagerial Auditing Journal
Volume18
Issue number8
DOIs
StatePublished - Nov 1 2003

Keywords

  • Decision-support systems
  • Financial reporting
  • Fraud
  • Fuzzy logic
  • Neural nets
  • Risk assessment

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