Title
Self-organizing fuzzy and MLP approaches to detecting fraudulent financial reporting
Publication Date
12-1-1996
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.
Publication Title
IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr)
Disciplinary Repository
SSRN
First Page
279
Last Page
285
Open Access Status
OA Disciplinary Repository
Recommended Citation
Feroz, Ehsan H. and Kwon, Taek Mu, "Self-organizing fuzzy and MLP approaches to detecting fraudulent financial reporting" (1996). Business Publications. 160.
https://digitalcommons.tacoma.uw.edu/business_pub/160
Source Full-text URL
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1215302
