Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts’ judgments

The objective of this study is to examine all aspects of fraud triangle using the data mining techniques and employ the available and public information to proxy variables to evaluate such attributes as pressure/incentive, opportunity, and attitude/rationalization, based on the findings from prior studies in this subject field and also the Statement on Auditing Standards. The second objective is to discuss whether or not the suggestion of the experts agrees with the results obtained from adopting those novel techniques. In specific, this study uses both expert questionnaires and data mining techniques to sort out the different fraud factors and then rank the importance of them. The data mining methods employed in this research include Logistic Regression, Decision Trees (CART), and Artificial Neural Networks (ANNs). Empirically, the ANNs and CART approaches work with the training and testing samples in a correct classification rate of 91.2% (ANNs) & 90.4% (CART) and 92.8% (ANNs) & 90.3% (CART), respectively, which is more accurate than the logistic model that only reaches 83.7% and 88.5% of the correct classification in assessing the fraud presence. In addition, type II error of ANNs drops significantly to 23.9% from 43.3% and 27.8% compared to the ones using CART and logistic models. Finally, the differences between different data mining tools and expert judgments are also compared to provide more insights as a research contribution.