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A user-centered explainable artificial intelligence approach for financial fraud detection

Author

Listed:
  • Zhou, Ying
  • Li, Haoran
  • Xiao, Zhi
  • Qiu, Jing

Abstract

This paper aims to produce user-centered explanations for financial fraud detection models based on Explainable artificial intelligence (XAI) methods. By combining an ensemble predictive model with an explainable framework based on Shapley values, we develop a financial fraud detection approach that is accurate and explainable at the same time. Our results show that the explainable framework can meet the requirements of different external stakeholders by producing local and global explanations. Local explanations can help understand why a specific prediction is identified as fraud, and global explanations reveal the overall logic of the whole ensemble model.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pa:s1544612323006815
    DOI: 10.1016/j.frl.2023.104309
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    References listed on IDEAS

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    1. 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, Wiley Blackwell, vol. 58(1), pages 199-235, March.
    2. Yi Wei & Jianguo Chen & Carolyn Wirth, 2017. "Detecting fraud in Chinese listed company balance sheets," Pacific Accounting Review, Emerald Group Publishing Limited, vol. 29(3), pages 356-379, August.
    3. 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.
    4. Xi Chen & Yang Ha (Tony) Cho & Yiwei Dou & Baruch Lev, 2022. "Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data," Journal of Accounting Research, Wiley Blackwell, vol. 60(2), pages 467-515, May.
    5. Raffaella Calabrese & Paolo Giudici, 2015. "Estimating bank default with generalised extreme value regression models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(11), pages 1783-1792, November.
    6. Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
    7. Genre, Véronique & Kenny, Geoff & Meyler, Aidan & Timmermann, Allan, 2013. "Combining expert forecasts: Can anything beat the simple average?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 108-121.
    8. Kim, Tae Wan & Routledge, Bryan R., 2022. "Why a Right to an Explanation of Algorithmic Decision-Making Should Exist: A Trust-Based Approach," Business Ethics Quarterly, Cambridge University Press, vol. 32(1), pages 75-102, January.
    9. Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2022. "Explainable artificial intelligence for crypto asset allocation," Finance Research Letters, Elsevier, vol. 47(PB).
    10. Achakzai, Muhammad Atif Khan & Juan, Peng, 2022. "Using machine learning Meta-Classifiers to detect financial frauds," Finance Research Letters, Elsevier, vol. 48(C).
    11. Jing Wu & Zhaocheng Zhang & Sean X. Zhou, 2022. "Credit Rating Prediction Through Supply Chains: A Machine Learning Approach," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1613-1629, April.
    12. Hongwen Han & Jiali Jenna Tang & Qingquan Tang, 2021. "Goodwill Impairment, Securities Analysts, and Information Transparency," European Accounting Review, Taylor & Francis Journals, vol. 30(4), pages 767-799, August.
    13. 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.
    14. Kevin K. Li & Richard G. Sloan, 2017. "Has goodwill accounting gone bad?," Review of Accounting Studies, Springer, vol. 22(2), pages 964-1003, June.
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