Effective Machine Learning-Based Predictive Analytics for Loan Default Prediction in Banking Sector

These days, a crucial topic is the early prediction of loan failure in banks. Customer loan default may be predicted using a variety of statistical and AI classification methods. This paper introduces a machine learning-based predictive analytics model aimed at improving loan default prediction in the banking sector by leveraging open P2P loan data from Lending Club. To improve the precision and consistency of predictions, the model uses state-of-the-art ML methods, namely RF. By analyzing critical borrower information such as credit history, loan purpose, and debt status, the model identifies high-risk borrowers with greater precision. The study evaluates a performance of different ML models, including LR, DT, and SVM, but identifies RF as the most effective, demonstrating superior metrics with 89% accuracy, 99.5% sensitivity, and 80.3% specificity. These results highlight the model’s ability to not only predict defaults accurately but also reduce false positives and negatives.