Towards Explainability of Machine Learning Models in Insurance Pricing

Kevin Kuo and Daniel Lupton

Papers from arXiv.org

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.

Date: 2020-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ias
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2003.10674

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