Loan Default Prediction Using Machine Learning

Abstract

Loan default prediction plays a critical role in the financial sector by helping lending institutions evaluate the creditworthiness of applicants and mitigate the risk of non-performing loans. With the growth of digital lending and the availability of large-scale financial datasets, machine learning (ML) algorithms offer powerful predictive capabilities. This paper proposes a data-driven model for predicting loan default probability and determining customer eligibility based on demographic, financial, and behavioral features. The methodology integrates feature selection, data preprocessing, and classification using advanced ML models such as Logistic Regression, Random Forest, XGBoost, and Neural Networks. Comparative performance analysis demonstrates the effectiveness of ensemble-based methods in improving accuracy and recall.