Using the artificial neural network to predict fraud litigation: Some empirical evidence from emerging markets

Authors:
Hsueh-Ju Chen;Shaio-Yan Huang;Chung-Long Kuo
Affiliations:
Department of Accounting, National Chung Hsing University, Taichung, Taiwan;Department of Accounting, Feng Chia University, Taichung, Taiwan;KPMG CPA Firm, Tainan, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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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.