Credit Risk Assessment in Banking using Machine Learning

Abstract

In the modern digital economy, the banking sector is undergoing a transformation, driven by rapid technological advancements and evolving customer expectations. At the heart of this transformation lies the pressing need to assess and mitigate credit risk more accurately and efficiently. Traditional credit scoring systems—although functional—often struggle to capture the nuances of today's dynamic financial environments. With the increasing availability of alternative and behavioural data, Machine Learning (ML) has emerged as a revolutionary force in redefining credit risk assessment. This research paper explores the practical and theoretical dimensions of using ML in Indian banking, with a focus on improving accuracy, reducing Non- Performing Assets (NPAs), and ensuring financial inclusion. Through a detailed survey involving 52 professionals and a comprehensive review of secondary literature, this study provides a grounded perspective on the current status, challenges, and future potential of ML- driven credit assessment. It not only identifies key technical and regulatory bottlenecks but also presents human-centric insights into how professionals perceive this shift. The findings underline a critical balance between automation and ethical oversight, urging banks to integrate ML responsibly for long-term resilience and trust.