Abstract
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.
Highlights
Financial statements are the basic documents that reflect the financial status of a company [1,2,3,4,5,6]
Financial statements fraud seriously damages the sustainable development of enterprises and financial markets [5,6,11,14]
For data mining techniques used in this study, the artificial neural network (ANN) and support vector machine (SVM) are suitable for selecting important variables; classification and regression tree (CART), chi-square automatic interaction detector (CHAID), C5.0, and quick unbiased efficient statistical tree (QUEST) are suitable for classifying, predicting, and detecting variables
Summary
Financial statements are the basic documents that reflect the financial status of a company [1,2,3,4,5,6]. Financial statements are the main basis of decision-making for the investing public, creditors, stakeholders, and other users of accounting information. They are statements of listed companies about their operating performance, financial status, and social responsibilities. Numerous cases of financial reporting fraud have occurred in both the U.S and Taiwan. Examples in Taiwan include Procomp Informatics, Summit Technology, Infodisc Technology, and ABIT Computer in 2004, Rebar in 2007, and XPEC Entertainment in 2016. It is, essential to develop methods to detect fraudulent activities early on
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