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

作者：Jan, Chyan-long^*

来源：Sustainability, 2018, 10(2): 513.

DOI：10.3390/su10020513

• 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)

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.

• 出版日期2018-2
• 单位苏州大学