Predicting Bank Loan Eligibility with Machine Learning : A Comparative Study of Advanced Algorithms

This paper presents a unique way for determining client loan eligibility utilizing powerful machine learning algorithms and data from several institutions. It focuses on the crucial phases of data purification, attribute selection, and comparative study of several machine learning approaches such as Gradient Boosting, CatBoost, XGBoost, AdaBoosting, and LightGBM. The study emphasizes the value of ensemble models in enhancing forecast accuracy and lowering default risk. Using cutting-edge machine learning techniques, this study offers banks and financial institutions with a powerful tool for improving loan eligibility checks. The suggested technique is shown to be reliable and effective through empirical validation and practical implementations.