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Bankruptcy filings are as high today as ever, calling into question the efficacy of existing bankruptcy prediction models. This paper tries to provide an alternative for bankruptcy prediction by using neuro fuzzy, a hybrid approach combining the functionality of fuzzy logic and the learning ability of neural networks. The empirical results show that neuro fuzzy demonstrates a better accuracy rate, lower misclassification cost and higher detecting power than does logit regression, meaning neuro fuzzy could be a great help in providing warnings of impending bankruptcy. Also, its comprehensive explanation about mapping functions among variables presumably provides a foundation for further development of theory and testing of the membership function shape, the transfer function, the methods to aggregate, the methods to defuzzify, and so on. [Copyright &y& Elsevier]

Abstract: Detecting corporate fraud and assessing the relative risk factors have been significant issues confronting the auditing profession for decades. This study therefore aims to apply a neural network system to predict fraud litigation for assisting accountants on audit strategy making. The empirical results show that neural network provides not only a promising predicting accuracy, but also a better detecting power and a less misclassification cost comparing with that of a logit model and auditor judgments. This suggests that an artificial intelligence technique is quite well in identifying a fraud-lawsuit presence, and hence could be a supportive tool for practitioners. Further, a remarkable finding related to the greater effects of management’s capability on fraud commitments acquires an attentive investigation of ethic issues in emerging markets where contribute the most important force in the global economy nowadays. [Copyright &y& Elsevier]