Explainable Artificial Intelligence in Financial Decision-Making: A Review of Methods, Applications, and Regulatory Challenges

Abstract

The increasing adoption of Artificial Intelligence (AI) and Machine Learning (ML) in financial institutions has improved predictive capabilities while simultaneously creating concerns regarding the interpretability and transparency of automated decisions. In highly regulated financial environments, understanding why an AI model produces a particular decision is critical for risk management, auditing, consumer protection, and regulatory compliance. This review examines the role of Explainable Artificial Intelligence (XAI) in financial decision-making, focusing on applications such as credit scoring, loan approval, fraud detection, market prediction, insurance assessment, and risk management. Major explanation techniques, including feature importance methods, local and global surrogate models, SHAP-based explanations, and counterfactual explanations, are reviewed and compared. The study investigates the trade-off between model accuracy and interpretability across traditional machine learning, ensemble models, and deep learning architectures. Particular attention is given to challenges involving fairness, bias, privacy, accountability, stability of explanations, and regulatory requirements. The review also considers how XAI can improve trust among customers, financial professionals, auditors, and regulators. Emerging research directions involving causal explanations, human-centered AI, responsible AI governance, and interpretable generative AI are discussed. The study concludes that explainability should be treated as a fundamental component of trustworthy AI rather than an optional feature in financial decision-making systems.