用可解释方法选特征，训练透明模型，兼顾精度与合规。
Enhancing ML Models Interpretability for Credit Scoring
- 先用SHAP选关键特征，再训透明模型
- 仅用10个特征达基准模型性能，减少88.5%
- 适合需合规解释的金融风控场景
预测违约对银行盈利与金融稳定至关重要。尽管现代机器学习方法常优于传统回归，但其缺乏透明性限制了在监管环境中的应用。可解释人工智能（XAI）在信用评分领域成为解决方案。然而，多数XAI研究聚焦于黑箱模型的事后解释，难以满足内部评级法（IRB）等监管要求。本文提出一种混合方法：利用黑箱模型的事后解释指导特征选择，再训练透明的玻璃盒模型，兼顾预测能力与可解释性。基于Lending Club数据集，该方法在仅使用10个特征的情况下，性能接近基准黑箱模型（使用88.5%更少特征）。实验中，采用SHAP进行特征选择，XGBoost作为基准黑箱模型，EBM和PLTR为考察的玻璃盒模型。通过特征交互分析、相关性检查与专家反馈，进一步提升模型可解释性与鲁棒性。
原文摘要 · Abstract (English)
Predicting default is essential for banks to ensure profitability and financial stability. While modern machine learning methods often outperform traditional regression techniques, their lack of transparency limits their use in regulated environments. Explainable artificial intelligence (XAI) has emerged as a solution in domains like credit scoring. However, most XAI research focuses on post-hoc interpretation of black-box models, which does not produce models lightweight or transparent enough to meet regulatory requirements, such as those for Internal Ratings-Based (IRB) models. This paper proposes a hybrid approach: post-hoc interpretations of black-box models guide feature selection, followed by training glass-box models that maintain both predictive power and transparency. Using the Lending Club dataset, we demonstrate that this approach achieves performance comparable to a benchmark black-box model while using only 10 features - an 88.5% reduction. In our example, SHapley Additive exPlanations (SHAP) is used for feature selection, eXtreme Gradient Boosting (XGBoost) serves as the benchmark and the base black-box model, and Explainable Boosting Machine (EBM) and Penalized Logistic Tree Regression (PLTR) are the investigated glass-box models. We also show that model refinement using feature interaction analysis, correlation checks, and expert input can further enhance model interpretability and robustness.
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