SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
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This paper examines published data to develop a model of Logistic Regression for detecting factors associated with Fraudulent Financial Statement (FFS). After an exhaustive exploitation of prior work used financial ratios, 21 ratios are selected as potential predictors of FFS and a series of experiments have been conducted to determine the optimal parameters for Logistic model. Then, we propose an appropriate model for detecting FFS of listed companies in China and compare its predictive ability with other detecting models using a data set of 174 listed companies in China including 87 with FFS and 87 with non-FFS during the period 1993-2007. The results demonstrate that the predictive ability of the model proposed in this paper is higher than other models at about 10% by using the optimal parameters determined and indicate the importance of financial ratios, which could benefit both internal and external auditors, taxation and other state authorities.