Enhancing ML Models Interpretability for Credit Scoring
Sagi Schwartz, Qinling Wang, Fang Fang
预测违约对于银行确保盈利能力和金融稳定至关重要。 虽然现代机器学习方法通常优于传统的回归技术,但它们缺乏透明度限制了他们在受监管环境中的使用。 可解释的人工智能(XAI)已经成为信用评分等领域的解决方案。 然而,大多数XAI研究都集中在黑箱模型的后解释上,黑箱模型不能产生足够轻或透明的模型,以满足监管要求,例如基于内部评级(IRB)模型的模型。 本文提出了一种混合方法:黑盒模型的后解释指导功能选择,然后训练玻璃盒模型,同时保持预测能力和质量。 使用Lending Club数据集,我们证明这种方法实现了与基准黑盒模型相当的性能,同时仅使用10个特征 - 一个88.5我们还展示了使用特征交互分析,相关性检查和专家输入的模型改进可以进一步提高模型可解释性和鲁棒性。
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 t...