用可解释的机器学习提升信贷评分，符合银行监管要求。
Machine and Deep Learning for Credit Scoring: A compliant approach
- 采用XGBoost等梯度提升模型替代传统评分模型。
- 性能显著提升，违约捕获率明显提高。
- 通过Shapley值解释模型，满足监管透明性要求。
信贷评分是银行和金融机构日常面临的核心问题。尽管机器学习与深度学习在金融领域的信贷评分研究中已取得令人瞩目的成果，但在银行这类高度监管的环境中尚未实际应用。本文旨在挑战现有监管现状，提出符合BASEL 2和3标准的新方法，同时满足美联储和欧洲央行的要求。我们借助梯度提升机（尤其是XGBoost）对某银行BANK A用于汽车贷款申请人的现有评分模型进行对比测试，证明使用此类算法能显著提升模型表现和违约捕获率。此外，我们利用Shapley值揭示这些相对简单的模型并非如现行监管体系所认为的那样‘黑箱’，并尝试在BANK A的模型设计与验证框架内解释模型输出与信用评分。
原文摘要 · Abstract (English)
Credit Scoring is one of the problems banks and financial institutions have to solve on a daily basis. If the state-of-the-art research in Machine and Deep Learning for finance has reached interesting results about Credit Scoring models, usage of such models in a heavily regulated context such as the one in banks has never been done so far. Our work is thus a tentative to challenge the current regulatory status-quo and introduce new BASEL 2 and 3 compliant techniques, while still answering the Federal Reserve Bank and the European Central Bank requirements. With the help of Gradient Boosting Machines (mainly XGBoost) we challenge an actual model used by BANK A for scoring through the door Auto Loan applicants. We prove that the usage of such algorithms for Credit Scoring models drastically improves performance and default capture rate. Furthermore, we leverage the power of Shapley Values to prove that these relatively simple models are not as black-box as the current regulatory system thinks they are, and we attempt to explain the model outputs and Credit Scores within the BANK A Model Design and Validation framework
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。