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Financial Fraud Detection Model Based on Random Forest

Listed author(s):
  • Liu, Chengwei
  • Chan, Yixiang
  • Alam Kazmi, Syed Hasnain
  • Fu, Hao

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.

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File URL: https://mpra.ub.uni-muenchen.de/65404/1/MPRA_paper_65404.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 65404.

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Date of creation: 22 Apr 2015
Publication status: Published in International Journal of Economics and Finance 7.7(2015): pp. 178-188
Handle: RePEc:pra:mprapa:65404
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  1. Alam Kazmi, Syed Hasnain, 2015. "Brand the Pricing: Critical Critique," MPRA Paper 64984, University Library of Munich, Germany.
  2. J. V. Hansen & J. B. McDonald & W. F. Messier, Jr. & T. B. Bell, 1996. "A Generalized Qualitative-Response Model and the Analysis of Management Fraud," Management Science, INFORMS, vol. 42(7), pages 1022-1032, July.
  3. Alam Kazmi, Syed Hasnain, 2015. "Developments in Promotion Strategies Review on Psychological Streams of Consumers," MPRA Paper 65424, University Library of Munich, Germany, revised 05 May 2015.
  4. Chen, Gongmeng & Firth, Michael & Gao, Daniel N. & Rui, Oliver M., 2006. "Ownership structure, corporate governance, and fraud: Evidence from China," Journal of Corporate Finance, Elsevier, vol. 12(3), pages 424-448, June.
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