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Many countries ban insurers from using genetic test results in underwriting. One study [Howard, R. C. W. (2014). Report to CIA research committee: Genetic testing model: If the underwriters had no access to known results. Canadian Institute of Actuaries (CIA).] stated that such a ban in Canada would expose life insurers to adverse selection, causing premiums to increase by 12%. More than a quarter of this cost was attributable to a single disorder, Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC). We model ARVC in a life insurance market, following the methodology of [Haçarız, O., Kleinow, T. & Macdonald, A. S. (2021). Genetics, insurance and hypertrophic cardiomyopathy. Scandinavian Actuarial Journal 2021, 54–81.], including 'cascade' genetic testing (CGT), so the rôle of family history in underwriting is modelled explicitly. We review (in the Appendix) the published epidemiology of ARVC, in particular the existence of an effective treatment, which we also include in our model. Our results are consistent with those of [Macdonald, A. S. & Yu, F. (2011). The impact of genetic information on the insurance industry: Conclusions from the 'bottom-up' modelling programme. Astin Bulletin41(02), 343–376.] and [Haçarız, O., Kleinow, T. & Macdonald, A. S. (2021). Genetics, insurance and hypertrophic cardiomyopathy. Scandinavian Actuarial Journal 2021, 54–81.], namely, that in realistic scenarios premium increases would be negligible. We also consider the possibility of life settlement companies 'gaming' insurers by learning of adverse genetic test results, and conclude that to profit from purchasing policies from affected individuals, they would have to predict the future trajectory of the epidemiology of ARVC better than the epidemiologists themselves. [ABSTRACT FROM AUTHOR]

We specify a mathematical model of Hypertrophic Cardiomyopathy (HCM) and consider the potential costs arising from adverse selection in a life insurance market. HCM is a dominantly inherited heart disorder which is relatively common and has high mortality; a population prevalence of 0.2% and annual mortality hazard (force of mortality) of 1% have been widely cited. Adverse selection may arise if insurers may not take account of adverse DNA-based genetic tests for a causative mutation. A novel feature of our model is that it includes cascade genetic testing (CGT) in nuclear families. CGT is the form of testing used in HCM. Among other things, it implies that genetic testing occurs only if a family history exists. We find in most scenarios the premium increases necessitated by adverse selection to be very small – fractions of 1% – but we ask under what circumstances these might increase to appreciable levels. Insurers' inability to use family history in underwriting would have a large impact. We note that the epidemiology of HCM is still evolving and that 0.2% is likely to be a considerable underestimate of mutation prevalence, while recent estimates of annual mortality hazards are much less than 1%. The first of these in particular is likely to limit any adverse selection costs. [ABSTRACT FROM AUTHOR]

We revisit surplus on general life insurance contracts, represented by Markov models. We classify technical bases in terms of boundary conditions in Thiele's equation(s), allowing more general regulations than Scandinavian-style ‘first-order/second-order’ regimes, and replacing the traditional retrospective policy value. We propose a ‘canonical’ model with three technical bases (premium, valuation, accumulation) and show how each pair of bases defines premium loadings and surplus. Along with a ‘true’ or ‘real-world’ experience basis, this expands fundamental results of Ramlau-Hansen [(1988b). The emergence of profit in life insurance. Insurance: Mathematics and Economics, 7(4), 225–236]. We conclude with two applications: lapse-supported business; and the retrospectively-oriented regime proposed by Møller and Steffensen [(2007). Market-valuation methods in life and pension insurance. Cambridge University Press]. [ABSTRACT FROM AUTHOR]

Restrictions on insurance risk classification may induce adverse selection, which is usually perceived as a bad outcome, both for insurers and for society. However, a social benefit of modest adverse selection is that it can lead to an increase in 'loss coverage', defined as expected losses compensated by insurance for the whole population. We reconcile the concept of loss coverage to a utilitarian concept of social welfare commonly found in the economic literature on risk classification. For iso-elastic insurance demand, ranking risk classification schemes by (observable) loss coverage always give the same ordering as ranking by (unobservable) social welfare. [ABSTRACT FROM AUTHOR]