The Role of Machine Learning in Loan Default Prediction Trends, Challenges, and Future Directions

Abstract

Predicting loan default has become one of the most important areas of study in the financial world because of the need to lower credit risk and find long-term ways to lend money. While credit scoring systems and logistic regression have provided some minimal answers, these traditional statistical models do not succeed of capturing the complicated borrowers' nonlinear patterns and behaviors. With machine learning, novel predictive models have been presented that are more sophisticated in predicting risk, such as supervised, ensemble, deep learning and hybrid models, which greatly add to the precision and effectiveness of credit risk forecasting. This paper is a complete overview of the use of machine learning in predicting loan defaults by presenting the underlying methodology, data, and feature engineering approaches. Moreover, their use in banking and fintech, the use of big data and other sources of information, and the increased relevance of explainable and interpretable AI are also considered. Some of the most important challenges, like the imbalance of data, model transparency, and regulatory compliance, are outlined, and the new research directions of developing more precise, scalable, and understandable systems to guide financial decisions are discussed.