Application of Random Forest, Rough Set Theory, Decision Tree and Neural Network to Detect Financial Statement Fraud - Taking Corporate Governance into Consideration

Given that corporate financial statement fraud cases have been increasing in recent years, establishment of a model to effectively detect financial statement fraud has turned out to be one of the crucial issues. This study has integrated RF, RST, decision tree and back propagation network (BNP) to construct a corporate financial statement fraud detection model, so as to help auditor detect the signs of financial statement fraud before a fraud occurs in an enterprise. The study has established a new financial statement fraud detection model, for which the empirical result shows that, while analyzing the corporate financial statement fraud indexes considered and measured by RF, corporate governance indexes should also be included in financial statement fraud detection other than other financial indexes. In addition, it is also found that the corporate financial statement fraud detection model constructed by integrating RF and RST could also effectively enhance classification accuracy.

[1]  Young-Chan Lee,et al.  Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters , 2005, Expert Syst. Appl..

[2]  Constantin Zopounidis,et al.  Business failure prediction using rough sets , 1999, Eur. J. Oper. Res..

[3]  Xuedong Yan,et al.  Exploring precrash maneuvers using classification trees and random forests. , 2009, Accident; analysis and prevention.

[4]  Kurt Fanning,et al.  Neural Network Detection of Management Fraud Using Published Financial Data , 1998 .

[5]  Ching-Chiang Yeh,et al.  Going-concern prediction using hybrid random forests and rough set approach , 2014, Inf. Sci..

[6]  Patricia M. Dechow,et al.  Causes and Consequences of Earnings Manipulation: An Analysis of Firms Subject to Enforcement Actions by the SEC* , 1996 .

[7]  William R. Kinney,et al.  Characteristics of firms correcting previously reported quarterly earnings , 1989 .

[8]  C. Zopounidis,et al.  Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques , 2002 .

[9]  Michael Firth,et al.  Ownership structure, corporate governance, and fraud: Evidence from China , 2006 .

[10]  Obeua S. Persons.  Using Financial Statement Data To Identify Factors Associated With Fraudulent Financial Reporting , 2011 .

[11]  Yannis Manolopoulos,et al.  Data Mining techniques for the detection of fraudulent financial statements , 2007, Expert Syst. Appl..

[12]  D. Edwards,et al.  Statistical Analysis of Gene Expression Microarray Data , 2003 .

[13]  M. Beasley.  An Empirical Analysis of the Relation between Board of Director Composition and Financial Statement Fraud , 1998 .

[14]  Z. Rezaee.  Causes, consequences, and deterence of financial statement fraud , 2005 .

[15]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[16]  Joseph C. Ugrin,et al.  An Examination of the Relative Deterrent Effects of Legislated Sanctions on Attitudes About Financial Statement Fraud: A Policy Capturing Approach , 2013 .

[17]  Thomas E. McKee.  Developing a bankruptcy prediction model via rough sets theory , 2000 .

[18]  Jean-Michel Poggi,et al.  Variable selection using random forests , 2010, Pattern Recognit. Lett..

[19]  Kathleen A. Kaminski,et al.  Can financial ratios detect fraudulent financial reporting , 2004 .