Explainable Artificial Intelligence for Financial Decision-Making: A Comprehensive Review
Abstract
The increasing adoption of Artificial Intelligence in finance has raised critical concerns regarding transparency, accountability, and trustworthiness of automated financial decisions. This review paper investigates the evolution and significance of Explainable Artificial Intelligence (XAI) in financial applications including loan approval systems, portfolio optimization, fraud analytics, and credit scoring models. The study reviews prominent XAI techniques such as SHAP, LIME, counterfactual explanations, and interpretable neural networks used to enhance model transparency in finance. Existing literature is systematically analyzed to understand the trade-offs between predictive accuracy and explainability in AI-driven financial systems. The paper also examines regulatory perspectives from financial authorities emphasizing responsible AI and fair lending practices. Furthermore, challenges related to model complexity, bias mitigation, scalability, and stakeholder trust are critically discussed. The review concludes that explainable AI is becoming a foundational requirement for ethical and regulatory-compliant financial intelligence systems and presents opportunities for future research in transparent AI governance.
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