用集成深度学习提升信贷评分预测准确率
Enhanced Credit Score Prediction Using Ensemble Deep Learning Model
- 融合随机森林、XGBoost与TabNet，通过堆叠技术构建集成模型
- 在多个指标上优于单模型，显著提升信贷评分预测精度
- 适合金融风控、银行信贷系统优化的从业者参考
在当代经济体系中，信用评分对各类参与者至关重要。稳健的信用评估系统是商业银行及金融行业核心业务（如信用卡、贷款、投资）盈利的关键。本文结合已在现代银行系统中广泛应用的高性能模型XGBoost和LightGBM，与强大的TabNet模型，构建了一个由随机森林、XGBoost与TabNet组成的集成模型，并采用堆叠技术实现信用评分等级的精准判定。该方法突破了单一模型的局限，显著提升了信用评分预测的准确性。后续章节详细阐述所用技术，并通过精确率、召回率、F1值和AUC等指标进行全面验证。三者协同互补，展现出卓越的整体性能。
原文摘要 · Abstract (English)
In contemporary economic society, credit scores are crucial for every participant. A robust credit evaluation system is essential for the profitability of core businesses such as credit cards, loans, and investments for commercial banks and the financial sector. This paper combines high-performance models like XGBoost and LightGBM, already widely used in modern banking systems, with the powerful TabNet model. We have developed a potent model capable of accurately determining credit score levels by integrating Random Forest, XGBoost, and TabNet, and through the stacking technique in ensemble modeling. This approach surpasses the limitations of single models and significantly advances the precise credit score prediction. In the following sections, we will explain the techniques we used and thoroughly validate our approach by comprehensively comparing a series of metrics such as Precision, Recall, F1, and AUC. By integrating Random Forest, XGBoost, and with the TabNet deep learning architecture, these models complement each other, demonstrating exceptionally strong overall performance.
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