Detection of fraudulent financial statements using the hybrid data mining approach.

Suduan Chen

doi:10.1186/s40064-016-1707-6

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 %).

Highlights

Financial statements are a company’s basic documents that reflect its financial status (Beaver 1966; Ravisankar et al 2011)
The selected variables are processed in the second stage using Bayesian belief network (BBN), support vector machine (SVM) and artificial neural network (ANN) modeling and classification performance tests
SPSS Clementine is used as the software for decision tree (DT) variable selection, and classification and regression trees (CART) and Chi squared automatic interaction detector (CHAID) are used for variable selection

Summary

Introduction

Financial statements are a company’s basic documents that reflect its financial status (Beaver 1966; Ravisankar et al 2011). The financial statement is the main basis for decision -making on the part of a vast number of investors, creditors and other persons in need of accounting information, as well as a concrete expression of business performance, financial status and the social responsibility of listed companies and OTC companies. Examples include the Enron case in 2001, the WorldCom case in 2003 in the United States, and the ABIT Computer, Procomp, Infodisc and Summit Technology cases in 2004 in Taiwan. Given these incidents, it has become important to be able to detect fraudulent behavior prior to its occurrence

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Conclusion