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

被引:56
作者
Jan, Chyan-long [1 ]
机构
[1] Soochow Univ, Dept Accounting, 56 Sect 1,Kueiyang St, Taipei 10048, Taiwan
关键词
financial statements fraud; data mining; artificial neural network (ANN); support vector machine (SVM); decision tree; classification and regression tree (CART); chi-square automatic interaction detector (CHAID); C5.0; quick unbiased efficient statistical tree (QUEST); DATA MINING TECHNIQUES; SUPPORT VECTOR MACHINE; LISTED COMPANIES; PREDICTION; DISTRESS; RATIOS; BANKRUPTCY; FAILURE;
D O I
10.3390/su10020513
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study aims to establish a rigorous and effective model to detect enterprises' financial statements fraud for the sustainable development of enterprises and financial markets. The research period is 2004-2014 and the sample is companies listed on either the Taiwan Stock Exchange or the Taipei Exchange, with a total of 160 companies (including 40 companies reporting financial statements fraud). This study adopts multiple data mining techniques. In the first stage, an artificial neural network (ANN) and a support vector machine (SVM) are deployed to screen out important variables. In the second stage, four types of decision trees (classification and regression tree (CART), chi-square automatic interaction detector (CHAID), C5.0, and quick unbiased efficient statistical tree (QUEST)) are constructed for classification. Both financial and non-financial variables are selected, in order to build a highly accurate model to detect fraudulent financial reporting. The empirical findings show that the variables screened with ANN and processed by CART (the ANN + CART model) yields the best classification results, with an accuracy of 90.83% in the detection of financial statements fraud.
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页数:14
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