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 perhaps due to difficulty in distinguishing such firms from other firms not engaged in fraudulent reporting. The neural-network approach sheds some light on this problem due to the attributes that it requires minimum prior knowledge of the data and achieves a highly nonlinear computational model based on past experience (training). In this study, we employ seven red flags which are composed of four financial red flags and three turnover red flags in order to detect targets of the Securities and Exchange Commission's (SEC's) investigation of fraudulent financial reporting. The red flags are computed over 70 firms spread among various industrial sectors, and form the base data that is used for developing the computational prediction model. Multilayered perceptron computation of this data was able to predict the targets of the SEC investigated firms with an average of 88% accuracy in the cross-validation test. On the other hand, the same data computed by the logit program gave an average prediction rate of 47%.
Bibliographical noteFunding Information:
Manuscript received March 15, 1995; revised July 11, 1995 and August 14, 1995. This work was supported in part by a grant to E. Feroz from the University of Minnesota Graduate School.
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