An Optimised Greedy-Weighted Ensemble Framework for Financial Loan Default Prediction

This paper presents an Optimised Greedy-Weighted Ensemble framework for predicting loan defaults in credit risk management. The system combines multiple machine learning models with optimized weights and uses a neural network to learn how to best blend their predictions together. Testing on real lending data shows this approach achieves strong performance (80% AUC) and identifies key factors like income and debt ratios as important default predictors.