A Survey of Machine Learning Algorithms in Credit Risk Assessment

Abstract

Credit risk assessment is a critical process for financial institutions, designed to predict the likelihood of borrower default and reduce potential financial losses. Traditionally, credit scoring relied on statistical models; however, the advent of machine learning (ML) has significantly transformed these methods. Machine learning provides more accurate, scalable, and flexible solutions for analyzing vast amounts of financial data. This survey examines key ML algorithms—including decision trees, random forests, support vector machines, and deep neural networks—that are used in credit risk assessment. Additionally, the paper explores advanced strategies such as the integration of unsupervised and supervised learning techniques, the adoption of ensemble methods, and the incorporation of alternative data sources, such as social media and utility payments, to improve predictive accuracy. Challenges related to data imbalance, feature selection, model interpretability, and computational efficiency are also discussed. By comparing the strengths and weaknesses of various ML models, this review offers valuable insights into the practical application of these technologies, helping financial institutions implement more robust, transparent, and effective credit risk management systems.