Predictive analysis in insurance: An application of generalized linear mixed models

Rosy Oh, Nayoung Woo, Jae Keun Yoo, Jae Youn Ahn

Research output: Contribution to journalArticlepeer-review

Abstract

Generalized linear models and generalized linear mixed models (GLMMs) are fundamental tools for predictive analyses. In insurance, GLMMs are particularly important, because they provide not only a tool for prediction but also a theoretical justification for setting premiums. Although thousands of resources are available for introducing GLMMs as a classical and fundamental tool in statistical analysis, few resources seem to be available for the insurance industry. This study targets insurance professionals already familiar with basic actuarial mathematics and explains GLMMs and their linkage with classical actuarial pricing tools, such as the Bühlmann premium method. Focus of the study is mainly on the modeling aspect of GLMMs and their application to pricing, while avoiding technical issues related to statistical estimation, which can be automatically handled by most statistical software.

Original languageEnglish
Pages (from-to)437-451
Number of pages15
JournalCommunications for Statistical Applications and Methods
Volume30
Issue number5
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.

Keywords

  • generalized linear mixed model
  • generalized linear model
  • insurance
  • predictive analysis
  • premium
  • ratemaking, Buhlmann premium

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