A Hybrid Detecting Fraudulent Financial Statements Model Using Rough Set Theory and Support Vector Machines

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Title:	A Hybrid Detecting Fraudulent Financial Statements Model Using Rough Set Theory and Support Vector Machines
Authors:	Yeh, CC (Yeh, Ching-Chiang) Chi, DJ (Chi, Der-Jang) Lin, TY (Lin, Tzu-Yu) Chiu, SH (Chiu, Sheng-Hsiung)
Contributors:	會計系
Keywords:	Fraudulent financial statements rough set theory support vector machines
Date:	2016
Issue Date:	2017-04-17 13:22:09 (UTC+8)
Abstract:	The detection of fraudulent financial statements (FFS) is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. Although nonfinancial ratios are generally acknowledged as the key factor contributing to the FFS of a corporation, they are usually excluded from early detection models. The objective of this study is to increase the accuracy of FFS detection by integrating the rough set theory (RST) and support vector machines (SVM) approaches, while adopting both financial and nonfinancial ratios as predictive variables. The results showed that the proposed hybrid approach (RST+SVM) has the best classification rate as well as the lowest occurrence of Types I and II errors, and that nonfinancial ratios are indeed valuable information in FFS detection.
Relation:	CYBERNETICS AND SYSTEMS 卷: 47 期: 4 頁碼: 261-276
Appears in Collections:	[Department of Accounting & Graduate Institute of Accounting] periodical articles

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