This paper explores the effectiveness of Data Mining Classification techniques such as Bayesian Belief Networks, Decision Tree and Support Vector Machine in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated with FFS. First, we underline the importance of financial and non-financial factors that can be used in the identification of FFS. Second, a number of experiments have been conducted using these techniques which were
optimized using a data set of 60 fraud and non-fraud firms in the recent period 1998~2005. The results shows that the Bayesian Belief Network has better performance than the Decision Tree and Support Vector Machine.
Relation:
Asian Journal of Management and Humanity Sciences 3(1-4):15-30
