Machine Learning for Credit Risk Assessment in Banking: A Comprehensive Review

Abstract

Credit risk assessment is one of the most important functions in banking because inaccurate lending decisions can significantly affect financial stability and institutional profitability. The availability of large volumes of structured and alternative financial data has created new opportunities for Machine Learning (ML) techniques to improve credit scoring and default prediction. This review provides a comprehensive examination of ML-based approaches for credit risk assessment, including logistic regression, decision trees, random forests, support vector machines, gradient boosting, artificial neural networks, and deep learning models. The study compares traditional statistical credit scoring methods with machine learning approaches in terms of predictive accuracy, interpretability, scalability, and robustness. Applications involving probability of default, loss given default, borrower classification, early-warning systems, and loan portfolio risk are discussed. The review also considers the growing use of transaction data, behavioral information, and real-time streaming data in credit risk models. Major challenges involving data imbalance, model bias, explainability, privacy, concept drift, and regulatory requirements are analyzed. The potential of explainable AI, federated learning, and alternative data sources to improve responsible credit decision-making is also explored. Future research directions are proposed for developing accurate, transparent, adaptive, and regulatory-compliant credit risk systems.