Researches of Detection of Fraudulent Financial Statements Based on Data Mining
Dan Han · Journal of Computational and Theoretical Nanoscience · 2017
Financial statement fraud has been one of the biggest challenges in the modern business world. Financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. In this paper, 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 are explored. Our study investigates the usefulness of Data Mining techniques including Decision Trees, Neural Networks and Bayesian Belief Networks in the identification of fraudulent financial statements. At last, we compare the three models in terms of their performances.