Artificial Intelligence in Credit Risk Management: A Comparative Study of Traditional vs AI-based Models in Indian Banks

Abstract

Effective handling of credit risk is essential to the stability and profitability of the banking environment, particularly in the Indian banking environment context where non-performing assets (NPAs) are increasing, the regulatory environment is adversarial and technology is disruptive. The paper is investigating the comparison of the artificial intelligence (AI)-based credit risk assessment models with the traditional credit risk models within the Indian banks. The work is based on secondary sources and empirical insights by capturing the transformation of rules-based, human expert and statistics-driven methodologies to machine learning and deep learning models, differences in predictive performance, processing speed, interpretability, and serving of underserved borrowers. Findings indicate that AI-based approaches offer superior predictive performance and greater scalability, but they also raise issues of data quality, transparency (“black-box” risk), ethical bias, regulatory oversight and organisational readiness. The paper concludes by suggesting that Indian banks adopt a hybrid model combining human judgement with AI, strengthen data governance and model explainability, and develop regulatory frameworks for responsible AI deployment in credit risk.