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Advancing financial risk management: A transparent framework for effective fraud detection

Author

Listed:
  • Li, Wenjuan
  • Liu, Xinghua
  • Su, Junqi
  • Cui, Tianxiang

Abstract

Robust financial fraud detection is crucial for protecting assets and maintaining financial system integrity. Traditional models lack flexibility, while machine learning models are often complex and difficult to interpret. We propose an XGB-GP framework that combines Extreme Gradient Boosting (XGB) and Genetic Programming (GP) to create interpretable models, enhancing fraud detection. Our framework highlights the effectiveness of the financial indicator “Total Liabilities/Operating Costs” and outperforms traditional and machine learning models in detecting fraud, as demonstrated through analysis of data from the CSMAR database of Chinese publicly listed companies.

Suggested Citation

  • Li, Wenjuan & Liu, Xinghua & Su, Junqi & Cui, Tianxiang, 2025. "Advancing financial risk management: A transparent framework for effective fraud detection," Finance Research Letters, Elsevier, vol. 75(C).
    • Handle: RePEc:eee:finlet:v:75:y:2025:i:c:s1544612325001308
    DOI: 10.1016/j.frl.2025.106865
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    References listed on IDEAS

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    1. Liu, Zhengxu & Xie, Jing, 2025. "How does executive pay gap affect enterprises' financial fraud?," Finance Research Letters, Elsevier, vol. 85(PC).

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