American Journal of Data Science and Artificial Intelligence
XGBoost versus LightGBM for Loan Approval Prediction: A Controlled, Like-for-Like Comparison
Deciding whether to approve a loan application is one of the highest-stakes decisions a lender makes: approving a risky applicant invites default, while turning away a sound one loses a good customer. Predicting the approval decision accurately is therefore central to comprehensive credit management. Gradient boosting has become the dominant approach for such tabular credit data, and two libraries, XGBoost and LightGBM, account for most of its practical use. Although both are widely applied to credit-risk and loan- …