Financial Fraud Detection Model Based on Random Forest

Liu, Chengwei and Chan, Yixiang and Alam Kazmi, Syed Hasnain and Fu, Hao (2015): Financial Fraud Detection Model Based on Random Forest. Published in: International Journal of Economics and Finance , Vol. 7, No. 7 (25 June 2015): pp. 178-188.

Abstract

Business's accelerated globalization has weakened regulatory capacity of the law and scholars have been paid attention to fraud detection in recent years. In this study, we introduced Random Forest (RF) for financial fraud technique detection and detailed features selection, variables’ importance measurement, partial correlation analysis and Multidimensional analysis. The results show that a combination of eight variables has the highest accuracy. The ratio of debt to equity (DEQUTY) is the most important variable in the model. Moreover, we applied four statistic methodologies, including parametric and non-parametric models to construct detection models and concluded that Random Forest has the highest accuracy and the non-parametric models have higher accuracy than non-parametric models. However, Random Forest can improve the detection efficiency significantly and have an important practical implication.

• Item Type: MPRA Paper
• Language: English
• Keywords: Financial Fraud Detection, Random Forest, Ratio of debt to equity, Partial Correlation Analysis, Statistic methodologies, Parametric models
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; G - Financial Economics > G0 - General > G00 - General; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation; M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M2 - Business Economics > M21 - Business Economics

References

Abbott, L. J., & Parker, S. (2000). Auditor selection and audit committee characteristics. Auditing: A journal of practice & theory, 19(2), 47-66. http://dx.doi.org/10.2308/aud.2000.19.2.47

Alam Kazmi, Syed Hasnain (2015a) Developments in Promotion Strategies: Review on Psychological Streams of Consumers. International Journal of Marketing Studies, Vol. 7, No. 3, 129-138. http://dx.doi.org/10.5539/ijms.v7n3p129

Alam Kazmi, Syed Hasnain (2015b) Brand the Pricing: Critical Critique. International Journal of Marketing Studies, Vol. 7, No. 3, 125-128. http://dx.doi.org/10.5539/ijms.v7n3p125

Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. 10.1023/A:1010933404324

Chen, G., Firth, M., Gao, D. N., & Rui, O. M. (2006). Ownership structure, corporate governance, and fraud: Evidence from China. Journal of Corporate Finance, 12(3), 424-448. 10.1016/j.jcorpfin.2005.09.002

Cohen, J. R., Krishnamoorthy, G., & Wright, A. (2004). The corporate governance mosaic and financial reporting quality. Journal of Accounting literature, 87-152. https://www2.bc.edu/~cohen/Research/Research4.pdf

Fanning, K. M., & Cogger, K. O. (1998). Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting, Finance & Management, 7(1), 21-41. DOI:10.1002/(SICI)1099-1174(199803)7:1<21::AID-ISAF138>3.0.CO;2-K

Feroz, E. H., Park, K. J., & Pastena, V. (1991). The financial and market effects of the SEC's accounting and auditing enforcement releases. Journal of Accounting Research, 29, 107-142. http://www.jstor.org/stable/2491006

Fang K N, b, Jian-Bina W U, et al. A Review of Technologies on Random Forests[J]. Statistics & Information Forum, 2011. http://en.cnki.com.cn/Article_en/CJFDTOTAL-TJLT201103007.htm

Hansen, J., McDonald, J. B., Messier Jr, W., & Bell, T. B. (1996). A generalized qualitative-response model and the analysis of management fraud. Management Science, 42(7),1022-1032. http://dx.doi.org/10.1287/mnsc.42.7.1022

James, K. L. (2003). The effects of internal audit structure on perceived financial statement fraud prevention. Accounting Horizons, 17(4), 315-327. http://dx.doi.org/10.2308/acch.2003.17.4.315

Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995-1003. 10.1016/j.eswa.2006.02.016

Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R news, 2(3), 18-22. http://cogns.northwestern.edu/cbmg/LiawAndWiener2002.pdf

Persons, O. S. (2011). Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research (JABR),11(3),38-46. http://cluteinstitute.com/ojs/index.php/JABR/article/view/5858/5936

Ravisankar, P., Ravi, V., Raghava Rao, G., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems, 50(2), 491-500. http://dx.doi.org/10.1016/j.dss.2010.11.006

Spathis, C. T. (2002). Detecting false financial statements using published data: some evidence from Greece. Managerial Auditing Journal, 17(4), 179-191. http://dx.doi.org/10.1108/02686900210424321

Stice, J. D. (1991). Using financial and market information to identify pre-engagement factors associated with lawsuits against auditors. Accounting Review, 516-533. http://www.jstor.org/stable/pdf/247807.pdf?acceptTC=true

Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological methods, 14(4), 323. http://dx.doi.org/10.1037/a0016973

Summers, S. L., & Sweeney, J. T. (1998). Fraudulently misstated financial statements and insider trading: an empirical analysis. Accounting Review, 131-146. http://www.jstor.org/stable/248345

ZHANG, L.W.,& ZHANG ,X.D., LIU, S.R.& SUN,P. & WANG,T.L. (2014). The basic principle of random forest and its applications in ecology: a case study of Pinus yunnanensis. Acta Ecologica Sinica , 34(3), 650-659. http://link.springer.com/article/10.1007/s00267-011-9691-7