Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 62 references that are in the map. First posted on arXiv 7 Mar 2025.
Builds on
7 of 62 references.
References · 7
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2018Metrics for Explainable AI: Challenges and Prospectscited by 257
- 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 252
- 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Predictioncited by 245
- 2017Interpretable & Explorable Approximations of Black Box Modelscited by 54
- 2017Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysiscited by 46
- 2019AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networkscited by 39
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysiscited by 2
- 2026Toward Aristotelian Medical Representations: Backpropagation-Free Layer-wise Analysis for Interpretable Generalized Metric Learning on MedMNISTcited by 2
- 2025Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Predictioncited by 0
- 2026Toward Aristotelian Medical Representations: Backpropagation-Free Layer-wise Analysis for Interpretable Generalized Metric Learning on MedMNIST7 Apr 2026
- 2025Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Prediction13 Sep 2025
- 2025Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis24 Jul 2025