Towards Explainability of Machine Learning Models in Insurance Pricing
This work tackles the problem of model explainability for actuaries in property & casualty insurance, but it appears incremental as it builds on existing interpretability concepts.
The paper addresses the limited adoption of machine learning in insurance pricing due to lack of transparency, proposing a framework for model interpretability and illustrating it with a case study.
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.