  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 the standards of accuracy in credit default prediction,
setting a new benchmark for the industry. To overcome these challenges, we
present an Ensemble Methods framework comprising LightGBM, XGBoost, and
LocalEnsemble modules, each making unique contributions to amplify diversity
and improve generalization. By utilizing distinct feature sets, our methodology
directly tackles limitations identified in previous studies, with the
overarching goal of establishing a novel standard for credit default prediction
accuracy. Our experimental findings validate the effectiveness of the ensemble
model on the dataset, signifying substantial contributions to the field. This
innovative approach not only addresses existing obstacles but also sets a
precedent for advancing the accuracy and robustness of credit default
prediction models.
