Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework

Abstract

This study introduces a multi-stage cascaded machine learning framework for loan approval prediction, which addresses the inefficiencies inherent in traditional processes. The framework combines Gradient Boosting, Support Vector Machine, and XGBoost to refine predictions at each stage. This approach allows the framework to leverage the strengths of each technique in order to handle complex relationships and imbalanced data. The framework was tested on a comprehensive financial dataset, demonstrating the critical role of Gradient Boosting and traditional features like home ownership in improving predictive accuracy. The research illustrates the framework's capacity to augment credit risk assessment, curtail defaults, and facilitate decision-making, providing financial institutions with a robust instrument for operational efficiency and financial stability.