Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble

arXiv:2402.17979v1 Announce Type: cross
Abstract: In the realm of consumer lending, accurate credit default prediction stands as a critical element in risk mitigation and lending decision optimization. Extensive research has sought continuous improvement in existing models to enhance customer experiences and ensure the sound economic functioning of lending institutions. This study responds to the evolving landscape of credit default prediction, challenging conventional models and introducing innovative approaches. By building upon foundational research and recent innovations, our work aims to redefine …

abstract arxiv consumer continuous continuous improvement credit cs.ai cs.ce cs.lg customer customer experiences decision economic element ensemble improvement innovations lending lightgbm methodology optimization prediction research risk sound type xgboost