A Hybrid Detecting Fraudulent Financial Statements Model Using Rough Set Theory and Support Vector Machines. Issue 4 (18th May 2016)

• Record Type: Journal Article
• Authors: Yeh, Ching-Chiang; Chi, Der-Jang; Lin, Tzu-Yu; Chiu, Sheng-Hsiung
• Is Part Of: Cybernetics and systems. Volume 47:Issue 4(2016)
• Page Start: 261
• Page End: 276
• Publication Date: 2016-05-18
• Subjects: Fraudulent financial statements -- rough set theory -- support vector machines; Cybernetics -- Periodicals; System theory -- Periodicals; 003.5
• DOI: 10.1080/01969722.2016.1158553
• Languages: English
• ISSNs: 0196-9722

Abstract

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.