Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance
This review addresses the need for transparency in AI for finance practitioners and researchers, but it is incremental as it synthesizes existing literature without introducing new methods or data.
The paper conducted a systematic review of Explainable AI (XAI) applications in finance, identifying key methods like attention mechanisms and SHAP, and highlighting a reliance on post-hoc interpretability techniques while noting gaps in current systems.
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.