Name
Title | ||
|---|---|---|
A Hybrid Detecting Fraudulent Financial Statements Model Using Rough Set Theory and Support Vector Machines. |
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1080/01969722.2016.1158553 | Cybernetics and Systems |
Keywords | Field | DocType |
Fraudulent financial statements,rough set theory,support vector machines | Early detection,Data mining,Predictive variables,Support vector machine,Rough set,Artificial intelligence,Finance,Classification rate,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
47 | 4 | 0196-9722 |
Citations | PageRank | References |
4 | 0.44 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ching-Chiang Yeh | 1 | 160 | 8.87 |
| Der-Jang Chi | 2 | 67 | 3.70 |
| Tzu-Yu Lin | 3 | 4 | 0.44 |
| Sheng-Hsiung Chiu | 4 | 4 | 0.44 |