论文标题
在保险定价中解释机器学习模型
Towards Explainability of Machine Learning Models in Insurance Pricing
论文作者
论文摘要
近年来，机器学习方法吸引了精算师的兴趣。但是，与广义线性模型相比，从业人员的采用受到限制，部分原因是这些方法缺乏透明度。在本文中，我们讨论了对财产和伤亡保险比例制造中的模型可解释性的需求，提出了一个解释模型的框架，并提出了一个案例研究以说明框架。
Machine learning methods have garnered increasing interest among actuaries in recent years. However, their adoption by practitioners has been limited, partly due to the lack of transparency of these methods, as compared to generalized linear models. In this paper, we discuss the need for model interpretability in property & casualty insurance ratemaking, propose a framework for explaining models, and present a case study to illustrate the framework.
