Zhang, Y., Ji, L. orcid.org/0000-0002-7790-7765, Aivaliotis, G. et al. (1 more author) (2024) Bayesian CART models for insurance claims frequency. Insurance: Mathematics and Economics, 114. pp. 108-131. ISSN 0167-6687
Abstract
The accuracy and interpretability of a (non-life) insurance pricing model are essential qualities to ensure fair and transparent premiums for policy-holders, that reflect their risk. In recent years, classification and regression trees (CARTs) and their ensembles have gained popularity in the actuarial literature, since they offer good prediction performance and are relatively easy to interpret. In this paper, we introduce Bayesian CART models for insurance pricing, with a particular focus on claims frequency modelling. In addition to the common Poisson and negative binomial (NB) distributions used for claims frequency, we implement Bayesian CART for the zero-inflated Poisson (ZIP) distribution to address the difficulty arising from the imbalanced insurance claims data. To this end, we introduce a general MCMC algorithm using data augmentation methods for posterior tree exploration. We also introduce the deviance information criterion (DIC) for tree model selection. The proposed models are able to identify trees which can better classify the policy-holders into risk groups. Simulations and real insurance data will be used to illustrate the applicability of these models.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Bayesian CART; Claims frequency; DIC; Insurance pricing; MCMC; Negative binomial distribution; Zero-inflated Poisson distribution |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Jan 2024 15:32 |
Last Modified: | 16 Jan 2024 15:32 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.insmatheco.2023.11.005 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:206885 |