Towards Explainability of Machine Learning Models in Insurance Pricing

• URL: http://arxiv.org/abs/2003.10674v1
• Date: Tue, 24 Mar 2020 05:51:30 GMT
• Title: Towards Explainability of Machine Learning Models in Insurance Pricing
• Authors: Kevin Kuo, Daniel Lupton
• Abstract summary: We discuss the need for model interpretability in property & casualty insurance ratemaking. We propose a framework for explaining models, and present a case study to illustrate the framework.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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