Detection of Fraudulent Financial Statements through the use of Data Mining Techniques

This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. In accomplishing the task of management fraud detection, auditors could be facilitated in their work by using data mining techniques. This study investigates the usefulness of Decision Trees, Neural Networks and Bayesian Belief Networks in the identification of fraudulent financial statements. The input vector is composed of ratios derived from financial statements. The three models are compared in terms of their performances. The results identify the model with the best accuracy rate and highlight the importance of variables in fraudulent financial statement detection. They also indicate that the investigation of financial information can be of use in the identification of FFS and underline the importance of financial ratios.