Predicting business failure under the existence of fraudulent financial reporting
Purpose – The objective of this paper is to stress the importance of detecting financial frauds in predicting business failures disclosed by the unexpected financial crisis brought by Enron, Worldcom and other corporate distresses involving accounting irregularities. Design/methodology/approach – The most frequently used methodologies in predicting business failures, discriminant analysis and neural network (NN) (based on the Kolmogorov-Gabor polynomial Volterra series algorithm) are used. This paper suggests a two-stage NN procedure: the first stage detected the false financial statements, which were excluded from samples that used to predict the business failures at the second stage. The one-stage discriminant analysis and the NN model are used to contrast the two-stage approach in terms of accuracy rate. Findings – The one-stage NN model has a higher accuracy rate in identifying failed firms than the discriminant analysis, while the two-stage NN approach has an even higher accuracy rate than the one-stage NN model. Practical implications – Detecting the fraudulent reporting in advance can effectively improve the accuracy rate of business failure predictions. Originality/value – The paper draws attention to the importance of excluding fraudulent financial reporting to increase the accuracy rate in predicting business failures.
Business failures, Financial reporting, Fraud, Neural nets
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