Machine Learning for Credit Risk Assessment in Indian Banking: Predictive Performance, Model Explainability, & the Road to Regulatory Acceptance

Machine Learning for Credit Risk Assessment in Indian Banking: Predictive Performance, Model Explainability, & the Road to Regulatory Acceptance

Abstract

A transformation has taken place in the financial sector as a result of technology in the world of finance; specifically, machine learning is changing how a lender evaluates a prospective borrower. This paper describes how technology advances the achievement of financial inclusion. The study assesses: debt approval process, interest rates charged to borrowers, and the level of financial inclusion achieved by borrowers through the use of automated credit scoring systems of non-banking financial institutions (NBFC). Data was collected and analysed from several sources, including RBI documents, World Bank studies, and industry reports. Findings suggest that the use of automated credit scoring has led to a reduction in the cost of credit and thus consequently improves the ability of previously unserved populations to access credit through less discriminatory feelings, greater liquidity, and more predicable repayment plans.

Authors

Mr. Ashish Diwakar, Ajay Kumar Sahu, Akanksha Aishwariyam, Akanksha Gupta, Akash Kumar

Institution

Noida Institute of Engineering & Technology (MCA Institute), Greater Noida

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