Predictive Analytics in Loan Default Prediction Using Machine Learning

Abstract

Predictive analytics plays a pivotal role in financial decision-making, particularly in loan default prediction, where early identification of potential defaulters can significantly mitigate risks for lending institutions. This paper explores the application of machine learning (ML) techniques in predicting loan defaults by analyzing borrower profiles, transaction histories, and other relevant financial indicators. Traditional credit scoring models, often limited in their ability to handle complex, non-linear relationships among variables, are increasingly being supplemented or replaced by advanced ML models. The study employs algorithms such as logistic regression, decision trees, random forests, support vector machines (SVM), and gradient boosting machines (GBM) to construct predictive models. Data preprocessing, including feature selection and handling of imbalanced datasets, is emphasized to enhance model accuracy and robustness. Comparative analysis of these models highlights their performance based on key metrics such as accuracy, precision, recall, and area under the receiver operating characteristic (ROC) curve.