Estimating Territory Risk Relativity for Auto Insurance Rate Regulation using Generalized Linear Mixed Models

Shengkun Xie, Chong Gan, Clare Chua-Chow

2021

Abstract

Territory risk analysis has played an essential role in auto insurance rate regulation. It aims to obtain a set of regions to estimate their respective relativities to reflect the regional risk. Cluster as a latent variable has not yet been considered in modelling the regional risk of auto insurance. In this work, spatially constrained clustering is first applied to insurance loss data to form such regions. The generalized linear mixed model is then proposed to derive the risk relativities for obtained clusters and then for each basic rating unit. The results are compared to the ones from generalized linear models. The Forward Sortation Area (FSA) grouping to a specific region by spatially constrained clustering is to reduce the insurance rate heterogeneity caused by some smaller number of risk exposures. The spatially constrained clustering and risk relativity estimation help obtain a set of territory risk benchmarks, which can be used in rate filings within the regulation process. It also provides guidance for auto insurance companies on rate-making. The proposed methodologies could be helpful and applicable in many other fields, including business data analytic.

Paper Citation

in Harvard Style

Xie S., Gan C. and Chua-Chow C. (2021). Estimating Territory Risk Relativity for Auto Insurance Rate Regulation using Generalized Linear Mixed Models. In Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-521-0, pages 329-334. DOI: 10.5220/0010601003290334

in Bibtex Style

@conference{data21,
author={Shengkun Xie and Chong Gan and Clare Chua-Chow},
title={Estimating Territory Risk Relativity for Auto Insurance Rate Regulation using Generalized Linear Mixed Models},
booktitle={Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2021},
pages={329-334},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010601003290334},
isbn={978-989-758-521-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Estimating Territory Risk Relativity for Auto Insurance Rate Regulation using Generalized Linear Mixed Models
SN - 978-989-758-521-0
AU - Xie S.
AU - Gan C.
AU - Chua-Chow C.
PY - 2021
SP - 329
EP - 334
DO - 10.5220/0010601003290334