论文标题
信用评分中机器学习模型的透明度，可调性和解释性
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring
论文作者
论文摘要
信用评分模型的主要要求是提供最大准确的风险预测。此外，监管机构要求这些模型是透明和可审计的。因此，在信用评分中，仍然广泛使用了非常简单的预测模型，例如逻辑回归或决策树，现代机器学习算法的出色预测能力不能完全杠杆化。因此，遗漏了巨大的潜力，导致更高的储量或更多信用违约。本文提出了不同的维度，这些维度必须考虑以使信用评分模型可以理解，并为使``黑匣子''机器学习模型透明，可审核和可解释的框架提供了一个框架。在此框架之后，我们介绍了技术的概述，展示了如何在信用评分中应用它们以及结果与计分卡的可解释性相比。一项现实世界的案例研究表明，在机器学习技术保持其提高预测能力的能力时，可以实现可比程度的解释性。
A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, regulators demand these models to be transparent and auditable. Thus, in credit scoring, very simple predictive models such as logistic regression or decision trees are still widely used and the superior predictive power of modern machine learning algorithms cannot be fully leveraged. Significant potential is therefore missed, leading to higher reserves or more credit defaults. This paper works out different dimensions that have to be considered for making credit scoring models understandable and presents a framework for making ``black box'' machine learning models transparent, auditable and explainable. Following this framework, we present an overview of techniques, demonstrate how they can be applied in credit scoring and how results compare to the interpretability of score cards. A real world case study shows that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power.
