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Statistical motor rating: making effective use of your data

Published online by Cambridge University Press:  20 April 2012

M. J. Brockman  and
T. S. Wright
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Abstract

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.

Keywords

General InsuranceMotorPricingStatistical Analysis
Type
Research Article
Information
Journal of the Institute of Actuaries , Volume 119 , Issue 3 , 1992 , pp. 457 - 543
Copyright
Copyright © Institute and Faculty of Actuaries 1992

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References

REFERENCES

Aitkin, M., Anderson, D., Francis, B. & Hind, J. (1989). Statistical Modelling in GLIM, Oxford Science Publications.Google Scholar
Almer, B. (1957). Risk Analysis in Theory and Practical Statistics. 15th International Congress of Actuaries, 2, 314.Google Scholar
Bailey, R. A. & Simon, L. (1960). Two Studies in Automobile Insurance Ratemaking. ASTIN Bulletin, 1, 192.CrossRefGoogle Scholar
Baxter, L. A., Coutts, S. M. & Ross, G. A. F. (1979). Applications of Linear Models in Motor Insurance. 21st International Congress of Actuaries, 2, 11.Google Scholar
Beard, R. E., Pentikainen, T. & Pesonen, E. (1984). Risk Theory, the Stochastic Basis of Insurance (3rd edition). Chapman and Hall.Google Scholar
Bennett, M. C. (1978). Models in Motor Insurance. J.S.S. 22, 134.Google Scholar
Coutts, S. M. (1984). Motor Insurance Rating: an Actuarial Approach. J.I.A. 111, 87.Google Scholar
Haberman, S. & Renshaw, A. E. (1988). Generalised Linear Models in Actuarial Work, Staple Inn Actuarial Society paper.Google Scholar
Healy, M. J. R. (1988). GLIM: An Introduction. Oxford Clarendon Press.Google Scholar
Johnson, P. D. & Hey, G. B. (1971). Statistical Studies in Motor Insurance. J.I.A. 97, 199.Google Scholar
Juno, J. (1968). On Automobile Insurance Ratemaking. ASTIN Bulletin, 5, 41.Google Scholar
Little, R. J. A. (1978). Generalised Linear Models for Cross Classified Data from the WFS, World Fertility Survey, technical bulletin.Google Scholar
McCullagh, P. & Nelder, J. A. (1983) and (1989). Generalised Linear Models, 1st and 3rd editions. Chapman and Hall.Google Scholar
Numerical Algorithms Group. (1985). Generalised Linear Interactive Modelling.Google Scholar
Renshaw, A. E. (1991). Actuarial Graduation Practice and Generalised Linear and Non-Linear Models. J.I.A. 118, 295.Google Scholar
Wedderburn, R. W. M. (1974). Quasi-Likelihood Functions, Generalised Linear Models and the Gauss-Newton Method. Biometrika, 61, 439–47.Google Scholar