Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912700094742528 |
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| author | Mohsin, Md Talha Nasim, Nabid Bin |
| author_facet | Mohsin, Md Talha Nasim, Nabid Bin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05966 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance Mohsin, Md Talha Nasim, Nabid Bin General Finance Artificial Intelligence 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. |
| title | Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance |
| topic | General Finance Artificial Intelligence |
| url | https://arxiv.org/abs/2503.05966 |