For estimates of critical illness (CI) insurance premiums in the presence of a known mutation leading to a genetic disorder, the key quantity is the penetrance, that is the probability q(x) that the disease has developed by age x. This function is often estimated in the genetics literature, though typically with large confidence intervals. In this paper we suggest that the main features of real penetrance functions can be represented reasonably well by simple one-parameter families of functions, which can be scaled to fit the age range and lifetime penetrance. This gives a simple, direct, pragmatic way to obtain quick estimates of CI premium rates from published penetrance estimates, and also some indicative bounds for such premium rates, which are useful since confidence intervals usually cannot be estimated. To aid this process, as a short-cut to the solution of Thiele's equations in a multiple-state model, we give extensive tables in another report (Macdonald & Yang, 2003).

The General Insurance Premium Rating Issues Working Party (GRIP) was established by the General Insurance Board of the Faculty and Institute of Actuaries in 2005 to review actuarial involvement in premium rating issues, pricing being one of the key areas in which actuaries work.

GRIP published a full report in January 2007, which is available at www.actuaries.org.uk/grip. This short paper summarises the recommendations of that full report. Further background, discussion and the rationale for these recommendations are set out in more detail in the full report.

The UK Biobank project is a proposed large-scale investigation of the combined effects of genotype and environmental exposures on the risk of common diseases. It is intended to recruit 500,000 subjects aged 40-69, to obtain medical histories and blood samples at outset, and to follow them up for at least 10 years. This will have a major impact on our knowledge of multifactorial genetic disorders, rather than the rare but severe single-gene disorders that have been studied to date. What use may insurance companies make of this knowledge, particularly if genetic tests can identify persons at different risk? We describe here a simulation study of the UK Biobank project. We specify a simple hypothetical model of genetic and environmental influences on the risk of heart attack. A single simulation of UK Biobank consists of 500,000 life histories over 10 years; we suppose that case-control studies are carried out to estimate age-specific odds ratios, and that an actuary uses these odds ratios to parameterise a model of critical illness insurance. From a large number of such simulations we obtain sampling distributions of premium rates in different strata defined by genotype and environmental exposure. We conclude that the ability of such a study reliably to discriminate between different underwriting classes is limited, and depends on large numbers of cases being analysed.

This paper shows how credibility theory can be encompassed within the theory of Hierarchical Generalized Linear Models. It is shown that credibility estimates are obtained by including random effects in the model. The framework of Hierarchical Generalized Linear Models allows a more extensive range of models to be used than straightforward credibility theory. The model fitting and testing procedures can be carried out using a standard statistical package. Thus, the paper contributes a further range of models which may be useful in a wide range of actuarial applications, including premium rating and claims reserving.

This paper gives a method for premium rating by postcode area. The method is based on spatial models in a Bayesian framework and uses the Gibbs sampler for estimation. A summary of the theory of Bayesian spatial methods is given and the data which was analysed by Taylor (1989) is reanalysed. An indication is given of the wide range of models within this class which would be suitable for insurance data. The aim of the paper is to introduce the models and to show how they can be utilised in an insurance setting.

A practical method of allowing for covariates in compound Poisson modelling distributions is discussed.