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.
Original language | English (US) |
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Pages | 279-285 |
Number of pages | 7 |
State | Published - 1996 |
Event | Proceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr - New York, NY, USA Duration: Mar 24 1996 → Mar 26 1996 |
Other
Other | Proceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr |
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City | New York, NY, USA |
Period | 3/24/96 → 3/26/96 |