Models in Motor Insurance.

Bennett, M. C.

doi:10.1017/s0020269x00009038

Cited by 6 publications

(5 citation statements)

References 2 publications

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“…Claims frequency within portfolio 7.3.1. Review Statistical modelling techniques for claim frequency data are well documented, e.g. Ferrara (1971) and Bennett (1978). Many models have been used where the dependent variable was either the number of claims or claim proportions and the independent variables were the underwriter's rating factors.…”

Section: Claims Frequency: Overall Levelsmentioning

confidence: 99%

Motor insurance rating, an actuarial approach

Coutts¹

1984

J. Inst. Actuar.

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This paper is largely based on a Ph.D. thesis (Coutts (1983)) and provides an actuarial approach to the technical aspects of motor insurance premium rating, where equal weights are given to both the practical and statistical elements of the problem. The methods described are applicable to the competitive United Kingdom motor insurance premium market. However, it is also contended that in countries where motor rating tariffs are in operation, the analyses proposed are still necessary for management to judge where in the portfolio the business is potentially unprofitable.

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Section: Claims Frequency: Overall Levelsmentioning

confidence: 99%

Motor insurance rating, an actuarial approach

Coutts¹

1984

J. Inst. Actuar.

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“…Os estudos sobre seguro de automóveis utilizando modelagem estatística se iniciaram na década de 60 (BAILEY; SIMON, 1960;JUNG, 1968) e 70 (BENNETT, 1978;HEY, 1971) e abordavam, principalmente, a análise do retorno financeiro de uma carteira de segurados.…”

Section: Modelos Aplicados a Dados De Sinistrosunclassified

Modelos De Poisson Inflada De Zeros E Binomial Negativa Inflada De Zeros Na Previsão De Sinistro De Automóveis

Zaniboni

Montini

2015

E&G

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<p>As seguradoras estão constantemente aprimorando seus modelos de sinistralidade para obter preços mais competitivos. A distribuição de probabilidade dos sinistros possui alta frequência no valor zero, possibilitando o ajuste das distribuições Poisson Inflada de Zeros (ZIP) e Binomial Negativa Inflada de Zeros (ZINB). O objetivo do trabalho é preencher duas lacunas da literatura em relação a estes modelos: verificar se fatores humanos e externos afetam o número de sinistros e estimar tanto a probabilidade de ocorrência do sinistro quanto o número de sinistros por meio do ajuste de modelos ZIP e ZINB para o número de sinistros das carteiras de seguro de automóveis. A base de dados foi extraída do sistema AUTOSEG, da SUSEP, e as variáveis sexo, estado de residência e idade do condutor, categoria tarifária e ano do modelo do automóvel, número de expostos e importância segurada média foram significativas nos modelos ajustados a um nível de 10%. O modelo de ZIP apresentou erro quadrático médio menor que os modelos ZINB, Poisson e Binomial Negativa. </p>

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“…Such models have become an established part of the description of claim frequency rates and average claim costs in motor insurance-as evidenced by a number of papers, including Johnson and Hey 0) , Grimes (2) , Bennett (3) , Baxter et al w and Coutts <5) . However, the use of generalized linear models in actuarial work is relatively new.…”

Section: Generalized Linear Models In Actuarial Work By S Haberman Amentioning

confidence: 99%

Generalized Linear Models in Actuarial Work

Haberman¹,

Renshaw²

1990

J Staple Inn Actuar Soc

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THE use of classical linear models in actuarial work is not new. Such models have become an established part of the description of claim frequency rates and average claim costs in motor insurance-as evidenced by a number of papers, including Johnson and Hey 0) , Grimes (2) , Bennett (3) , Baxter et al. w and Coutts <5) . However, the use of generalized linear models in actuarial work is relatively new. Thus, McCullagh and Nelder <6) give a number of examples of the fitting of generalized linear models to different types of data. One of these relates to data from Baxter et alS 4) on the average claim costs in a motor insurance portfolio (originally modelled by Baxter et al. using a weighted least-squares approach). We made a small step in the direction of using generalized linear models in life insurance when we modelled the variation of lapse rates with age at entry, duration of policy, type of policy and insurance company' 7 '. Some of these models are described further in this paper.The purpose of the paper is to show that generalized linear models have a wide area of application in actuarial work and are not confined merely to models for motor insurance premiums. This purpose is fulfilled by demonstrating three separate practical applications in actuarial work:(i) fitting of loss distributions in non-life insurance (Section 3); (ii) representing the variation in force of mortality in life insurance underwriting (Section 4); and (iii) representing the variation in lapse rates with policy characteristics in life insurance (Section 5). Each of these applications involves different types of data and a different type of model. Section 3 is concerned with the fitting of skewed loss distributions. It shows how to adapt the GLIM package to fit certain types of distribution which are not immediately available within the package. Three examples are the Pareto, Burr and Weibull distributions. It is a trivial matter to fit other loss distributions such as the log normal, gamma and log gamma to uncomplicated data.The applications discussed in Section 4 are particularly novel. The GLiM-based approach outlined here could pave the way for a completely new, scientifically sound approach to life insurance underwriting. It offers a more dynamic means of model building than has hitherto been attempted in this field. The models allow the effects of individual factors and their interactions on excess mortality may be assessed. We would highlight the meager assumptions on which the models are based, the comparative ease with which they can be fitted and compared using GLIM and the appealing connection which these models have with the traditional actuarial approach using standard mortality ratios.terms of use, available at https://www.cambridge.org/core/terms. https://doi

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Models in Motor Insurance.

Cited by 6 publications

References 2 publications

Motor insurance rating, an actuarial approach

Motor insurance rating, an actuarial approach

Modelos De Poisson Inflada De Zeros E Binomial Negativa Inflada De Zeros Na Previsão De Sinistro De Automóveis

Generalized Linear Models in Actuarial Work

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