Predicting Loan Defaults with Machine Learning: A Business Intelligence Approach to Responsible Lending

Abstract

This report presents a comprehensive analysis of loan default prediction using machine learning techniques applied to a curated dataset from a peer-to-peer lending platform. The study aims to enhance credit risk assessment by identifying high-risk loan applicants before loan issuance, thereby improving financial decision-making and promoting responsible lending practices. A subset of 2,468 anonymized loan records, containing borrower demographics, financial information, credit history, and loan status, served as the foundation for model development and evaluation. The research employs exploratory data analysis, principal component analysis, clustering, and supervised learning methods including logistic regression, decision trees, support vector machines (SVM), neural networks, and ensemble models. Key predictors of default, such as loan_percent_income, loan_int_rate, credit_score, and homeownership status, were consistently identified across models. Among the models tested, SVM demonstrated the highest validation AUC, indicating strong generalization capability, while logistic regression and decision trees provided interpretable, threshold-based insights for operational use. Clustering analysis revealed distinct borrower segments with varying risk profiles, offering a strategic advantage for personalized risk mitigation strategies. Despite the dataset’s anonymized nature and modest size, the models achieved strong predictive accuracy and actionable insights. The findings support the integration of machine learning into financial risk assessment pipelines and offer evidence-based recommendations for data driven lending. Ultimately, this study demonstrates how explainable and predictive machine learning models can be applied to real world financial datasets to support credit scoring, reduce default risk, and increase overall economic stability through more informed loan approval processes.