Analysis of Data Mining Techniques for Detection of Financial Statement Fraud
Nasib Singh Gill, Rajan Gupta · SSRN Electronic Journal · 2012
Top level management is usually found responsible for fraudulent reporting of financial statements to fulfill the objective of artificially improving the financial performance and results of the company. Several data mining algorithms have been implemented for successful identification of fraudulent financial reporting. This paper explores the four commonly used data mining techniques, viz., Neural Network, Decision Trees, Genetic Algorithms and Bayesian Belief Networks for detection of financial statement fraud. This study investigates the effectiveness of these four techniques in identifying fraudulent financial statements. In addition, the techniques were compared in terms of their performances based on eight varying parameters. Neural network appeared as the most extensively used technique for detection and identification of financial statement fraud.