Artificial Intelligence In Credit Risk Assessment And Loan Approval Processes

The rapid integration of Artificial Intelligence (AI) into credit risk assessment represents one of the most consequential transformations in contemporary financial services. This paper provides a comprehensive, interdisciplinary review of AI applications in credit scoring and loan approval—spanning machine learning algorithms, deep learning architectures, and Natural Language Processing techniques—while systematically examining the governance, ethical, and regulatory dimensions that determine whether such systems serve the public interest. Drawing on a synthesis of over 40 peer-reviewed sources and six international case studies spanning Upstart, Ant Financial, JPMorgan Chase, ZestFinance, HDFC Bank, and Kabbage, the study documents consistent AUC-ROC performance improvements of 5–15 percentage points for AI models over traditional logistic regression baselines. The research finds that ensemble methods—particularly XGBoost and LightGBM—dominate operational deployments due to their superior accuracy-interpretability balance, while deep learning architectures offer advantages in large-scale, temporally rich environments. The paper critically examines algorithmic bias, data privacy risks, the black-box interpretability challenge, and the evolving global regulatory landscape, proposing a Responsible AI Framework built on four pillars: Performance, Fairness, Transparency, and Accountability. The study offers targeted recommendations for financial institutions and regulators, with particular attention to the Indian market context including the Account Aggregator framework and UPI transaction data as transformative inputs for credit inclusion.