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Financial Fraud Detection Model Based on Random Forest

Author

Listed:
  • Liu, Chengwei
  • Chan, Yixiang
  • Alam Kazmi, Syed Hasnain
  • Fu, Hao

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.

Suggested Citation

  • Liu, Chengwei & Chan, Yixiang & Alam Kazmi, Syed Hasnain & Fu, Hao, 2015. "Financial Fraud Detection Model Based on Random Forest," MPRA Paper 65404, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:65404
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    File URL: https://mpra.ub.uni-muenchen.de/65404/1/MPRA_paper_65404.pdf
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    References listed on IDEAS

    as
    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. Feroz, Eh & Park, K & Pastena, Vs, 1991. "The Financial And Market Effects Of The Secs Accounting And Auditing Enforcement Releases," Journal of Accounting Research, Wiley Blackwell, vol. 29, pages 107-142.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Jianrong Yao & Yanqin Pan & Shuiqing Yang & Yuangao Chen & Yixiao Li, 2019. "Detecting Fraudulent Financial Statements for the Sustainable Development of the Socio-Economy in China: A Multi-Analytic Approach," Sustainability, MDPI, vol. 11(6), pages 1-17, March.
    2. Badal Khan & Muhammad Aqil & Syed Hasnain Alam Kazmi & Syed Imran Zaman, 2023. "Day‐of‐the‐week effect and market liquidity: A comparative study from emerging stock markets of Asia†," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 544-561, January.
    3. Mubarik, Muhammad Shujaat & Kazmi, Syed Hasnain Alam & Zaman, Syed Imran, 2021. "Application of gray DEMATEL-ANP in green-strategic sourcing," Technology in Society, Elsevier, vol. 64(C).
    4. Huosong Xia & Xiang Wei & Wuyue An & Zuopeng Justin Zhang & Zelin Sun, 2021. "Design of electronic-commerce recommendation systems based on outlier mining," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 295-311, June.
    5. Muhammad Asad Ali & Muhammad Aqil & Syed Hasnain Alam Kazmi & Syed Imran Zaman, 2023. "Evaluation of risk adjusted performance of mutual funds in an emerging market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1436-1449, April.
    6. Nouhaila Innan & Muhammad Al-Zafar Khan & Mohamed Bennai, 2023. "Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models," Papers 2308.05237, arXiv.org.
    7. Craja, Patricia & Kim, Alisa & Lessmann, Stefan, 2020. "Deep Learning application for fraud detection in financial statements," IRTG 1792 Discussion Papers 2020-007, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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    More about this item

    Keywords

    Financial Fraud Detection; Random Forest; Ratio of debt to equity; Partial Correlation Analysis; Statistic methodologies; Parametric models;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G00 - Financial Economics - - General - - - General
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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