Impact of genetic testing on life insurance

The Human Genome project generates immense interest in the scientific community though there are also important issues for the business community, particularly insurance companies. The dramatic advances in our understanding of the human genetic code, or human genome, affect our understanding of the determinants of human longevity and this is critical to the profitability of life insurance contracts. Insurance plays an important role in our economy. For example, there were 42 life insurance companies managing approximately AUD $188 billion as at 30 June 2002 and these same companies received AUD $38.1 billion in premium income over for the year ended 30 June 2002. Life insurance contracts are increasingly being sold in combination with superannuation where the contract forms part of the superannuation package. For example up to 85 per cent of all life office assets and 90 per cent of the premiums were classified as superannuation business in 2001 (APRA, 2002). Regardless of whether an individual submits an application form directly to a life insurance company as part of a stand alone policy, or indirectly via superannuation, the insurance company faces the question of deciding whether they wish to sell insurance to this individual. While life insurance policies can take a number of forms, an essential feature of these contracts is that they promise the payment of a given amount to certain beneficiaries when the insured dies. The time of death is critical to the pricing and profitability of these contracts. For insurance contracts to be profitable they must be priced so that invested premiums generate sufficient reserves to meet the payment of death benefits when they fall due. Failure to adequately model the impact of our increased understanding of the human genome could have a dramatic impact on the profitability of insurance contracts. The mapping of the human genome and the rapid development of genetic testing means that people have access to greater knowledge about their health and longevity yet this information may not be freely available, particularly to insurers and annuity providers (Hoy and Polborn, 2000). If individuals have more information about their health than insurance companies, this can complicate the pricing of life insurance contracts and annuities. This one-sided access to information is often referred to as information asymmetry and at its worst information asymmetry can lead to market failure (Akerlof, 1970). Further, where the insurer is unable to accurately assess the risk of an applicant it is possible that prices will be set too high. The ultimate result could be that only those applicants most likely to require a payout will purchase the product resulting in low profitability or losses. This is a critical problem in the pricing of life insurance products. Life insurers generally attempt to protect themselves through pricing for average risks though this may become more difficult where information about health and longevity is restricted in an asymmetric manner. An objective of this paper is to show that failure to adequately model the impact of our increased understanding of the human genome on choices made by consumers, and on the pricing policies of insurance companies could have a dramatic impact on the profitability of insurance contracts. While Doherty and Thistle (1996) and Hoy and Polborn (2000) provide economic analysis of the impact of information about the human genome, the work of actuaries such as Macdonald (1997) provides insight into the problems that actuaries face in the day-to-day pricing of life insurance contracts. We apply the MacDonald (1997) model to gain further understanding of the impact of genetic research on Australian life insurance contracts in particular, the effects on profitability of adverse selection by consumers who discover themselves to be high risk, and who subsequently purchase more insurance than would otherwise have been the case. The following section provides a brief review of the literature. It is followed by two sections that respectively describe the model used in the simulation of the insurance problem faced by Australian insurance companies, and the results of the simulation. Conclusions are drawn in the final section of the article.