USF St. Petersburg campus Faculty Publications
A fuzzy neural network for assessing the risk of fraudulent financial reporting.
Document Type
Article
Publication Date
2003
ISSN
0268-6902
Abstract
While the 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.
Publisher
Emerald
Recommended Citation
Lin, J.W., Hwang, M.I., & Becker, J.D. (2003). A fuzzy neural network for assessing the risk of fraudulent financial reporting. Managerial Auditing Journal, 18, 657-665. doi: 10.1108/02686900310495151
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Citation only. Full-text article is available through licensed access provided by the publisher. Members of the USF System may access the full-text of the article through the authenticated link provided.