A Comparative Perspective on Machine Learning and AI for Credit Risk Assessment in Banking: Regulatory Transparency and Decision Traceability

This paper explores the relevance of Machine Learning (ML) in credit risk assessment within the banking and financial sector, particularly in comparison to Artificial Intelligence (AI)-driven decisioning systems. While both technologies contribute to enhancing credit evaluation frameworks, ML offers greater transparency and traceability in model outputs, making it more suitable for regulated environments. One of the key advantages of ML lies in its ability to backtrack and analyse rejected applications—an essential requirement for compliance, especially when financial regulators like the Reserve Bank of India (RBI) demand justifications for rejection. Unlike black-box AI models, ML models provide segment level insights and allow for interpretable rule-based classification. This paper argues that while AI may assist in broader customer segmentation and behavioural prediction, it falls short in decision transparency. Thus, ML emerges as the more appropriate tool for actionable credit decisions in regulatory-bound systems, enabling institutions to ensure explainability, fairness, and accountability.