Predictive risk analysis using a collective risk model: Choosing between past frequency and aggregate severity information

Rosy Oh, Youngju Lee, Dan Zhu, Jae Youn Ahn

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The typical risk classification procedure in the insurance field consists of a priori risk classification based on observable risk characteristics and a posteriori risk classification where premiums are adjusted to reflect claim histories. While using the full claim history data is optimal in a posteriori risk classification, some insurance sectors only use partial information to determine the appropriate premium to charge. Examples include auto insurance premiums being calculated based on past claim frequencies, and aggregate severities used to decide workers’ compensation. The motivation is to have a simplified and efficient a posteriori risk classification procedure, customized to the context involved. This study compares the relative efficiency of the two simplified a posteriori risk classifications, that is, those based on frequency and severity. It provides a mathematical framework to assist practitioners in choosing the most appropriate practice.

Original languageEnglish
Pages (from-to)127-139
Number of pages13
JournalInsurance: Mathematics and Economics
StatePublished - Jan 2021

Bibliographical note

Funding Information:
Rosy Oh was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( 2019R1A6A1A11051177 and 2020R1I1A1A01067376 ). Jae Youn Ahn was supported by an NRF grant funded by the Korean Government ( 2020R1F1A1A01061202 ).

Publisher Copyright:
© 2020 Elsevier B.V.


  • A posteriori risk classification
  • Bühlmann premium
  • Collective risk model
  • Predictive analysis
  • Premium


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