Explainable Artificial Intelligence (XAI) In The Banking Sector: A Review Of Techniques For Transparent Credit, Fraud And Compliance Decisions

Banks increasingly use complex machine learning models such as neural networks, random forests and gradient boosting for credit scoring, loan approval, fraud detection, risk assessment and anti-money laundering. These models process sensitive financial data and directly affect people's access to credit and other essential services, yet many operate as black boxes that cannot explain why a loan was rejected or a transaction flagged. This raises concerns about fairness, bias, accountability and compliance with regulations that require meaningful justification of automated decisions. This paper reviews Explainable Artificial Intelligence (XAI) for banking. It examines why explainability is needed, then surveys inherently interpretable models such as logistic regression and decision trees, post-hoc techniques including LIME, SHAP, accumulated local effects and feature importance, and counterfactual explanations that tell customers what would change a decision. Applications in credit risk, fraud detection and customer decision support are discussed, together with fairness metrics and regulatory frameworks including the GDPR, the EU AI Act, model risk management guidance and the Reserve Bank of India's responsible AI framework. A layered XAI architecture for banking is proposed, with research gaps, objectives and a comparison of techniques. The review concludes that explainability is essential for transparent, ethical and reliable AI-driven banking.