Motor insurance rating, an actuarial approach

Coutts, S. M.

doi:10.1017/s0020268100041561

Cited by 18 publications

(27 citation statements)

References 16 publications

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“…Models such as those described in the subsequent sections have been used to describe claim rates in, for example, general insurance but not in life assurance. References from the United Kingdom actuarial literature include Johnson and Hey,(2) Grimes, (3) Bennett(4) and Coutts (5) We take advantage of the GLIM statistical package in carrying out the modelling required. Details are provided in § § 3, 4 and 5.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of life assurance lapses

Renshaw

Haberman

1986

J. Inst. Actuar.

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Data have been generously supplied by the Faculty of Actuaries Withdrawals Research Group. These data cover the lapse or withdrawal experience for the calendar year 1976 of seven Scottish life offices. An extensive analysis has been published by the Research Group although, for reasons we shall discuss later, we believe that the approach outlined here is better able to describe the structure of the data than the detailed (and somewhat pedestrian) tabulations of this earlier paper.

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Section: Introductionmentioning

confidence: 99%

Statistical analysis of life assurance lapses

Renshaw

Haberman

1986

J. Inst. Actuar.

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“…Risk premium for the i-th risk class, i=1,2,…,n, can be equated as the product of estimated claim frequency and estimated average claim cost (severity) for all claim categories [1][2][3][4][5]. As such, if we have three claim categories, the risk premium is…”

Section: Risk Premium Calculationmentioning

confidence: 99%

Premium analysis for copula model: A case study for Malaysian motor insurance claims

Resti

Ismail²,

Jaaman³

2014

AIP Conference Proceedings

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This study performs premium analysis for copula models with regression marginals. For illustration purpose, the copula models are fitted to the Malaysian motor insurance claims data. In this study, we consider copula models from Archimedean and Elliptical families, and marginal distributions of Gamma and Inverse Gaussian regression models. The simulated results from independent model, which is obtained from fitting regression models separately to each claim category, and dependent model, which is obtained from fitting copula models to all claim categories, are compared. The results show that the dependent model using Frank copula is the best model since the risk premiums estimated under this model are closely approximate to the actual claims experience relative to the other copula models.

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“…Bennett came down in favour of the additive model, largely on the grounds that it could be fitted relatively simply. For claim sizes, Baxter et al used an additive model, but failed to find a satisfactory error structure (a fact clear from their paper, but only later made explicit by Coutts (1984)). Baxter, Coutts & Ross (1980) attempted to remedy this.…”

Section: The Two Main Aspects Of Premium Ratingmentioning

confidence: 99%

Statistical motor rating: making effective use of your data

Brockman¹,

Wright²

1992

J. Inst. Actuar.

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The paper gives details of statistical modelling techniques which can be used to estimate risk and office premiums from past claims data. The methods described allow premiums to be estimated for any combinaton of rating factors, and produce standard errors of the risk premium. The statistical package GLIM is used for analysing claims experience, and GLIM terminology is used and explained thoughout the paper.Arguments are put forward for modelling frequency and severity separately for different claim types. Pitted values can be used to estimate risk premiums, and the incorporation of expenses allows for the estimation of office premiums. Particular attention is given to the treatment of no claim discount.The paper also discusses possible uses of the modelled premiums. These include the construction of 'standardised' one way tables and the analysis of experience by postal code and model of vehicle. Also discussed is the possibility of using the results for assessing the impact of competition, and for finding 'niche' markets in which an insurer can operate both competitively and profitably.

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Motor insurance rating, an actuarial approach

Cited by 18 publications

References 16 publications

Statistical analysis of life assurance lapses

Statistical analysis of life assurance lapses

Premium analysis for copula model: A case study for Malaysian motor insurance claims

Statistical motor rating: making effective use of your data

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