文章基本信息

标题：Machine Learning in Ratemaking, an Application in Commercial Auto Insurance
本地全文：下载
作者：Spencer Matthews ; Brian Hartman
期刊名称：Risks
印刷版ISSN：2227-9091
出版年度：2022
卷号：10
期号：4
页码：80
DOI：10.3390/risks10040080
语种：English
出版社：MDPI, Open Access Journal
摘要：This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS). These datasets include bodily injury (BI), property damage (PD) and collision (COLL) coverage, each documenting policy characteristics and claims across a four-year period. The datasets are explored, including summaries of all variables, then the methods for modeling are set forth. Models are tuned and the tuning results are displayed, after which we train the final models and seek to explain select predictions. Data were provided by a private insurance carrier to the CAS after anonymizing the dataset. These data are available to actuarial researchers for well-defined research projects that have universal benefit to the insurance industry and the public. Our hope is that the methods demonstrated here can be a good foundation for future ratemaking models to be developed and tested more efficiently.