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

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  • 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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    Cited by:

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    3. Wang, Muyun & Zhang, Ying, 2024. "Excess goodwill and enterprise litigation risk," Finance Research Letters, Elsevier, vol. 67(PB).
    4. Xu, Zhan & Wang, Solomon & Ye, Junchen, 2024. "The effect of digitization on corporate fraud detection evidence from China," International Review of Financial Analysis, Elsevier, vol. 96(PB).

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