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PurposeThe purpose is to explore the differences and similarities between fraudulent financial reporting detection and business failure prediction (BFP) models, especially in terms of which explanatory variables and methodologies are most effective.Design/methodology/approachIn total, 52 financial variables were identified from previous studies as potentially significant. A number of Taiwanese firms experienced financial distress or were accused of fraudulent reporting in 2005. Data on these firms and their contemporaries were obtained from the Taiwan Economic Journal data bank and Taiwan Stock Exchange Corporation. Financial variables were calculated for the years 2003 and 2004. Three well‐known data mining algorithms were applied to build detection/prediction models for this sample: logistic regression, neural networks, and classification trees.FindingsMany of the variables are effective at both detecting fraudulent financial reporting and predicting business failures. In terms of overall accuracy, logistic regression outperforms the other two algorithms for detecting fraudulent financial reporting. Whether logistic regression or a decision tree is best for BFP depends on the relative opportunity cost of misclassifying failing and healthy firms.Originality/valueThe financial factors used to detect fraudulent reporting are helpful for predicting business failure.

Hospitals and health care providers tend to get involved in exaggerated and fraudulent medical claims initiated by national insurance schemes. The present study applies data mining techniques to detect fraudulent or abusive reporting by healthcare providers using their invoices for diabetic outpatient services. This research is pursued in the context of Taiwan's National Health Insurance system. We compare the identification accuracy of three algorithms: logistic regression, neural network, and classification trees. While all three are quite accurate, the classification tree model performs the best with an overall correct identification rate of 99%. It is followed by the neural network (96%) and the logistic regression model (92%).

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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.