Article
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Preserved in Portico This version is not peer-reviewed
Machine Learning in Ratemaking, an Application in Commercial Auto Insurance
Version 1
: Received: 11 March 2022 / Approved: 15 March 2022 / Online: 15 March 2022 (10:59:32 CET)
A peer-reviewed article of this Preprint also exists.
Matthews, S.; Hartman, B. Machine Learning in Ratemaking, an Application in Commercial Auto Insurance. Risks 2022, 10, 80. Matthews, S.; Hartman, B. Machine Learning in Ratemaking, an Application in Commercial Auto Insurance. Risks 2022, 10, 80.
Abstract
This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS). These data sets include BI (bodily injury), PD (property damage) and COLL (collision) 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. All of the code will be made available on GitHub. Data was provided by a private insurance carrier to the CAS after anonymizing the data set. This data is 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.
Keywords
Ratemaking; Machine Learning; Explainability; Auto Insurance
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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