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An intelligent detecting model for financial frauds in Chinese A‐share market

Author

Listed:
  • Yunchuan Sun
  • Xiaoping Zeng
  • Ying Xu
  • Hong Yue
  • Xipu Yu

Abstract

Financial frauds can cause serious damage to financial markets but are hard to detect manually. In this study, we develop an intelligent detecting model to efficiently identify financial frauds by using XGBoost on raw financial data items in corporation financial statements. With listed companies in Chinese A‐share Market taken as samples, empirical results reveal that the proposed model works better than traditional models by a large margin in detecting fraud. Notably, the proposed model exhibits superior performance when used together with raw financial data items than with financial indicators. Moreover, the proposed model remains robust on outperformance in fraud detection when serial fraud cases are recoded, test periods are altered, more raw financial data are input, as well as other machine learning models–the AdaBoost and SVM–are selected as benchmark models. Our study enriches the application of machine learning in finance sector, and highlights the economic significance of raw financial data as the financial system's most fundamental components.

Suggested Citation

  • Yunchuan Sun & Xiaoping Zeng & Ying Xu & Hong Yue & Xipu Yu, 2024. "An intelligent detecting model for financial frauds in Chinese A‐share market," Economics and Politics, Wiley Blackwell, vol. 36(2), pages 1110-1136, July.
    • Handle: RePEc:bla:ecopol:v:36:y:2024:i:2:p:1110-1136
    DOI: 10.1111/ecpo.12283
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    References listed on IDEAS

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    1. David F. Larcker & Anastasia A. Zakolyukina, 2012. "Detecting Deceptive Discussions in Conference Calls," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 50(2), pages 495-540, May.
    2. Xin Xu & Feng Xiong & Zhe An, 2023. "Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework," Journal of Business Ethics, Springer, vol. 186(1), pages 137-158, August.
    3. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 58(1), pages 199-235, March.
    4. Serhan Cevik & Fedor Miryugin, 2022. "Death and taxes: Does taxation matter for firm survival?," Economics and Politics, Wiley Blackwell, vol. 34(1), pages 92-112, March.
    5. Jeremy Bertomeu & Edwige Cheynel & Eric Floyd & Wenqiang Pan, 2021. "Using machine learning to detect misstatements," Review of Accounting Studies, Springer, vol. 26(2), pages 468-519, June.
    6. Mark Cecchini & Haldun Aytug & Gary J. Koehler & Praveen Pathak, 2010. "Detecting Management Fraud in Public Companies," Management Science, INFORMS, vol. 56(7), pages 1146-1160, July.
    7. Karpoff, Jonathan M., 2021. "The future of financial fraud," Journal of Corporate Finance, Elsevier, vol. 66(C).
    8. Nerissa C. Brown & Richard M. Crowley & W. Brooke Elliott, 2020. "What Are You Saying? Using topic to Detect Financial Misreporting," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 58(1), pages 237-291, March.
    9. Kees Camfferman & Jacco L. Wielhouwer, 2019. "21st century scandals: towards a risk approach to financial reporting scandals," Accounting and Business Research, Taylor & Francis Journals, vol. 49(5), pages 503-535, July.
    10. Michaël Aklin & Eric Arias & Julia Gray, 2022. "Inflation concerns and mass preferences over exchange‐rate policy," Economics and Politics, Wiley Blackwell, vol. 34(1), pages 5-40, March.
    11. Kexing Ding & Baruch Lev & Xuan Peng & Ting Sun & Miklos A. Vasarhelyi, 2020. "Machine learning improves accounting estimates: evidence from insurance payments," Review of Accounting Studies, Springer, vol. 25(3), pages 1098-1134, September.
    12. Steven Toms, 2019. "Financial scandals: a historical overview," Accounting and Business Research, Taylor & Francis Journals, vol. 49(5), pages 477-499, July.
    13. Zhou, Ying & Li, Haoran & Xiao, Zhi & Qiu, Jing, 2023. "A user-centered explainable artificial intelligence approach for financial fraud detection," Finance Research Letters, Elsevier, vol. 58(PA).
    14. Salim Chahine & Yiwei Fang & Iftekhar Hasan & Mohamad Mazboudi, 2021. "CEO Network Centrality and the Likelihood of Financial Reporting Fraud," Abacus, Accounting Foundation, University of Sydney, vol. 57(4), pages 654-678, December.
    15. Dan Amiram & Serene Huang & Shiva Rajgopal, 2020. "Does financial reporting misconduct pay off even when discovered?," Review of Accounting Studies, Springer, vol. 25(3), pages 811-854, September.
    16. Pushan Dutt & Ilia Tsetlin, 2021. "Income distribution and economic development: Insights from machine learning," Economics and Politics, Wiley Blackwell, vol. 33(1), pages 1-36, March.
    17. Md Jahidur Rahman & Hongtao Zhu, 2023. "Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3455-3486, September.
    18. Achakzai, Muhammad Atif Khan & Peng, Juan, 2023. "Detecting financial statement fraud using dynamic ensemble machine learning," International Review of Financial Analysis, Elsevier, vol. 89(C).
    19. Xin‐Ping Song & Zhi‐Hua Hu & Jian‐Guo Du & Zhao‐Han Sheng, 2014. "Application of Machine Learning Methods to Risk Assessment of Financial Statement Fraud: Evidence from China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 611-626, December.
    20. Simi Kedia & Thomas Philippon, 2009. "The Economics of Fraudulent Accounting," The Review of Financial Studies, Society for Financial Studies, vol. 22(6), pages 2169-2199, June.
    21. Li, Jing & Li, Nan & Xia, Tongshui & Guo, Jinjin, 2023. "Textual analysis and detection of financial fraud: Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 126(C).
    22. Patricia M. Dechow & Weili Ge & Chad R. Larson & Richard G. Sloan, 2011. "Predicting Material Accounting Misstatements," Contemporary Accounting Research, John Wiley & Sons, vol. 28(1), pages 17-82, March.
    23. Anastasia A. Zakolyukina, 2018. "How Common Are Intentional GAAP Violations? Estimates from a Dynamic Model," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 56(1), pages 5-44, March.
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