A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

Abstract

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

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.…”

“…As mentioned in previous studies, compared to the traditional approach of regression analysis, data mining techniques are more rigorous and accurate in detecting financial statements fraud [3][4][5][6]11,14]. In terms of contributions to academic research and theoretical implications, this study conducts a new successful research for using hybrid data mining techniques to detect the fraud of enterprises' financial statements and provides several rigorous and effective financial statements fraud detection models different from previous studies in the literature.…”

“…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.…”

“…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].…”