Predicting Loan Defaults Using Machine Learning A Comparative Study
DOI:
https://doi.org/10.5281/zenodo.22874832Keywords:
Machine Learning In Decision Support, Loan Default Prediction, Credit Risk Modeling, Open-Source Financial Data, Lending Club Dataset, Statistical Learning Models, Neural Network Approaches, Model Performance Evaluation, Information-Theoretic Metrics, Regulatory-Compliant AI, Model Interpretability, Tree-Based Learning Methods, Banking Analytics Deployment, Practical ML Adoption, Explainable Credit Models, Financial Risk Assessment, Operational ML Systems, Compliance-Aware Modeling, Predictive Analytics In Banking, Data-Driven Lending Decisions.Abstract
Machine learning has become increasingly central to decision-support systems, driving extensive research into its application across diverse sectors. This study leverages publicly available loan default data to train and compare a range of machine learning models, from traditional statistical approaches to complex neural networks, evaluating their performance using information-theoretic and statistical measures. Beyond model performance, we examine practical considerations essential to real-world adoption within a banking environment.
While academic research has largely driven advances in machine learning algorithms, the practical focus has now shifted toward deployment, interpretability, regulatory compliance, and operational feasibility. Using publicly available data from Lending Club, a leading U.S. provider of loans to individuals and small businesses, we develop machine learning models to predict loan defaults. The final deployed solution employs tree-based methods, chosen for their interpretability and alignment with regulatory requirements.
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