Analyzing the Performance of Ensemble Machine Learning Algorithms for Predicting Loan Eligibility - C, Santhosh Kumar; S, Manishankar; Reddy, Perla Madhava; Gopal, Keertan (2024 9th International Conference on Communication and Electronics Systems (ICCES), 2024)
Loans are becoming increasingly important in the banking sector, but traditional methods of assessing them based mainly on asset value and income often fall short in identifying reliable borrowers. Our research addresses this gap by using machine learning techniques to predict loan eligibility more effectively. We apply algorithms such as Decision Tree, Extra Trees, XGBoost, and LightGBM, with LightGBM achieving an optimal accuracy of 98.91%, to analyze data and produce more accurate predictions. We also introduce a new feature that allows users to input branch-specific details, enabling customized loan assessments based on different bank criteria within each branch. This feature helps users identify banks most likely to approve their loan applications. After predictions are made, the platform displays the locations of the relevant bank branches. Our system allows users to apply for loans online and provides detailed information on various loan types and branch-specific requirements. This streamlined approach reduces loan defaults, accelerates approvals, and offers clients more suitable credit options tailored to their financial circumstances.