A Study for Detecting Enterprise Financial Statement Fraud

ASIAIR > Asia University Journals > Asian Journal of Management and Humanity Sciences > v.3 n.1-4 > Item 310904400/2132

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/2132

Title:	A Study for Detecting Enterprise Financial Statement Fraud
Authors:	CHING-CHIANG YEH DER-JANG CHI SIN-JIN LIN
Contributors:	Department of Business Administration, National Taipei College of Business
Keywords:	fraudulent financial statements;data mining;Bayesian Belief Network;Decision Tree;Support Vector Machine
Date:	2008
Issue Date:	2009-10-13 08:36:38 (UTC+0)
Publisher:	Asia University
Abstract:	This paper explores the effectiveness of Data Mining Classification techniques such as Bayesian Belief Networks, Decision Tree and Support Vector Machine in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated with FFS. First, we underline the importance of financial and non-financial factors that can be used in the identification of FFS. Second, a number of experiments have been conducted using these techniques which were optimized using a data set of 60 fraud and non-fraud firms in the recent period 1998~2005. The results shows that the Bayesian Belief Network has better performance than the Decision Tree and Support Vector Machine.
Relation:	Asian Journal of Management and Humanity Sciences 3(1-4):15-30
Appears in Collections:	[Asian Journal of Management and Humanity Sciences] v.3 n.1-4

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