A dependent frequency–severity approach to modeling longitudinal insurance claims

Gee Y. Lee and Peng Shi

Insurance: Mathematics and Economics, 2019, vol. 87, issue C, 115-129

Abstract: In nonlife insurance, frequency and severity are two essential building blocks in the actuarial modeling of insurance claims. In this paper, we propose a dependent modeling framework to jointly examine the two components in a longitudinal context where the quantity of interest is the predictive distribution. The proposed model accommodates the temporal correlation in both the frequency and the severity, as well as the association between the frequency and severity using a novel copula regression. The resulting predictive claims distribution allows to incorporate the claim history on both the frequency and severity into ratemaking and other prediction applications. In this application, we examine the insurance claim frequencies and severities for specific peril types from a government property insurance portfolio, namely lightning and vehicle claims, which tend to be frequent in terms of their count. We discover that the frequencies and severities of these frequent peril types tend to have a high serial correlation over time. Using dependence modeling in a longitudinal setting, we demonstrate how the prediction of these frequent claims can be improved.

Keywords: Frequency–severity model; Gaussian copula; Longitudinal insurance claims; Nonlife insurance; Predictive modeling (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:87:y:2019:i:c:p:115-129

DOI: 10.1016/j.insmatheco.2019.04.004

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