梳理金融领域可解释AI研究脉络，揭示当前技术依赖与短板。
Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance
- 通过文献计量与内容分析，系统归纳金融XAI应用模式。
- 注意力机制、特征重要性与SHAP是主流可解释方法。
- 适合金融+AI交叉研究者参考，关注解释力与实操落地。
在追求模型精度与透明度平衡的背景下，可解释人工智能（XAI）成为金融领域的重要交汇点。本文通过文献计量与内容分析，全面梳理金融领域XAI的应用演变、领域特定实现、方法论进展及研究趋势。结果表明，当前研究高度依赖后验解释技术，其中注意力机制、特征重要性分析和SHAP是最常用的方法。研究强调需融合金融知识与先进可解释范式，同时指出现有XAI系统存在显著缺陷，亟需跨学科协同改进。
原文摘要 · Abstract (English)
Practitioners and researchers trying to strike a balance between accuracy and transparency center Explainable Artificial Intelligence (XAI) at the junction of finance. This paper offers a thorough overview of the changing scene of XAI applications in finance together with domain-specific implementations, methodological developments, and trend mapping of research. Using bibliometric and content analysis, we find topic clusters, significant research, and most often used explainability strategies used in financial industries. Our results show a substantial dependence on post-hoc interpretability techniques; attention mechanisms, feature importance analysis and SHAP are the most often used techniques among them. This review stresses the need of multidisciplinary approaches combining financial knowledge with improved explainability paradigms and exposes important shortcomings in present XAI systems.
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