Detection of fraudulent financial statements using the hybrid data mining approach

Abstract

The purpose of this study is to construct a valid and rigorous fraudulent financial statement detection model. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements between the years 2002 and 2013. In the first stage, two decision tree algorithms, including the classification and regression trees (CART) and the Chi squared automatic interaction detector (CHAID) are applied in the selection of major variables. The second stage combines CART, CHAID, Bayesian belief network, support vector machine and artificial neural network in order to construct fraudulent financial statement detection models. According to the results, the detection performance of the CHAID–CART model is the most effective, with an overall accuracy of 87.97 % (the FFS detection accuracy is 92.69 %).

An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan

“…ANN and SVM are suitable for selecting important variables, while CART, CHAID, C5.0, and QUEST are suitable for classifying, predicting, and detecting variables [3][4][5][6]. In the first stage, the artificial neural network (ANN) and support vector machine (SVM) techniques are used to screen important variables.…”

“…Financial statements are the basic documents that reflect the financial status of a company [1][2][3][4][5][6]. Financial statements are also the main basis of decision-making for the investing public, creditors, stakeholders, and other users of accounting information.…”

“…They are also statements of listed companies about their operating performance, financial status, and social responsibilities. However, financial statement fraud seems to be occurring at an increasing pace and with a growing magnitude [5][6][7][8][9][10][11][12][13][14]. Financial statements fraud seriously damages the sustainable development of enterprises and financial markets [5,6,11,14].…”

“…Later, cash flow was also incorporated [19][20][21]. In recent years, corporate governance has been included as part of the equation for the prediction of financial crises or accounting fraud [3][4][5][6]10,11,[13][14][15]22,23]. In sum, the discriminatory models must add or delete variables over time to effectively identify the companies likely to report accounting fraud.…”

“…Machine learning and data mining with an extensive amount of data yields more accurate prediction and classification outcomes, compared to the traditional approach of regression analysis. Artificial neural networks (ANN), decision trees (DT), support vector machines (SVM), and Bayesian belief networks (BBN) are all popular tools for the detection of accounting fraud [3][4][5][6]11,14].…”