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.

翻译：在消费信贷领域，准确的信用违约预测是风险缓释和贷款决策优化的关键要素。大量研究致力于持续改进现有模型，以提升客户体验并确保贷款机构的经济稳健运行。本研究回应了信用违约预测领域的动态演变，挑战传统模型并引入创新方法。通过立足基础研究并融合最新进展，我们的工作旨在重新定义信用违约预测的精度标准，为行业树立新标杆。为克服现有挑战，我们提出了一种集成方法框架，该框架包含LightGBM、XGBoost和LocalEnsemble模块，每个模块通过独特贡献增强模型多样性并提升泛化能力。通过利用差异化特征集，我们的方法直接解决了先前研究中识别的局限性，其核心目标是建立信用违约预测准确性的新标准。实验结果表明，该集成模型在数据集上具有有效性，为该领域做出了显著贡献。这一创新方法不仅克服了现有障碍，更为推进信用违约预测模型的准确性与鲁棒性树立了典范。