Paper: arXiv 2503.05966
Authors: Md Talha Mohsin, Nabid Bin Nasim
Abstract
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.
Complexity vs Empirical Score
- Math Complexity: 1.5/10
- Empirical Rigor: 3.5/10
- Quadrant: Philosophers — conceptual discussion, limited math and data
Why this score: This paper is a qualitative and bibliometric systematic review of Explainable AI (XAI) in finance rather than a quantitative empirical study. It offers low mathematical complexity and lacks rigorous empirical backtesting or statistical quantitative analysis, functioning primarily as an overview of existing literature and techniques like SHAP and attention mechanisms.
Research Flowchart
flowchart TD
A["Research Goal:<br>Analyze XAI Applications & Methodologies in Finance"] --> B["Data Inputs:<br>Financial XAI Literature Corpus"]
B --> C["Methodology:<br>Bibliometric & Content Analysis"]
C --> D["Computational Process:<br>Topic Clustering & Trend Mapping"]
D --> E["Outcome 1:<br>Heavy Reliance on Post-Hoc Interpretability<br><i>(SHAP, Feature Importance, Attention)</i>"]
D --> F["Outcome 2:<br>Identified Critical XAI Gaps & Need for<br>Multidisciplinary Domain Integration"]