General Insurance Article - The challenges of future modelling of longevity risk

    Impact in the bulk purchase annuity market

    The bulk annuity market involves the transfer of liabilities from pension schemes to insurers, reinsurers or banks.  Historically, this market has been dominated by insurance companies buying out liabilities of pension schemes that became insolvent. Since 2006, financially healthy companies began to use the BPA market to manage the associated longevity risk in their pension funds using options such as a full buy out, a buy-in or more recently longevity swaps. This was largely due to potential changes in accounting rules and increased awareness by the managers of pension schemes on the options available to them to manage the pension scheme risks. Some examples of FTSE 100 companies, with relatively large pension schemes, above £300m, that have transferred their pension liabilities are shown in Table 2.  The larger pension schemes, which have their own mortality data, are able to submit this longevity risk information to an insurer or reinsurer, to secure better underwriting terms.

    

    The key challenge in the BPA market is assessing the uncertainty of using past experience of pension schemes to assess longevity risk. Most established models for the assessment of uncertainty of the schemes mortality experience, such as the Poisson model, assumes that the pension amount is evenly distributed among the schemes pensioners. However, it is quite common to have a high proportion of pension amounts concentrated in a relatively small number of people in that scheme. The concentration of pension amount to a small number of people increases the uncertainty around mortality experience and so stochastic models would have to be developed to measure this uncertainty. 

    Most pension schemes with liabilities below £1bn are expected to have fewer than 25,000 people. It is not uncommon to have half of the total pension amount belonging to about 10% of pensioners. At this level of concentration, a stochastic model estimates that the 95% confidence intervals for a 3-year mortality experience of a population with 5000 pensioners could be about ± 20%. The corresponding figure for a population of about 25,000 people is about ± 10%. A 10% error in assessing mortality rates may result in around a 3% change in the value of annuities for some schemes, and insurers may want to price taking account of this level of uncertainty.

    The use of this longevity modelling helps actuaries assess uncertainty relating to longevity risk and help insurers decide on the price for accepting the liabilities related to the pension scheme. Models that use information such as the postcodes of the individual scheme members, helps in modelling longevity risk. The results from this modelling may then be compared with the mortality experience provided by the pension scheme and, if required, further mathematical models may be developed to reconcile results between postcode modelling and scheme’s mortality experience.

    Impact of improvements in information

    The latest model developed by the Continuous Mortality Investigation (CMI) Bureau aims to extrapolate annual rates of improvement in mortality based on historical data of the total population in England and Wales. The CMI analysed the mortality trends of people in different socio-economic groups using mortality data from the English Longitudinal Study.  However the dataset covers only 1% of the population and mortality data is only analysed once every 4 to 5 years. The volume of data is too low and release of data too infrequent to provide a clear conclusion on differences in mortality trends of different socio-economic groups over the last 2 decades. So, it is currently unclear if annuitants of different socio-economic or health circumstances have experienced the same historical trends as the total population in the last 2 decades. More importantly, it is still uncertain how their mortality rates would change in the future. The availability of mortality data of people in different socio-economic circumstances from larger datasets with higher frequency of release, perhaps annually, would help elucidate these uncertainties.

    In addition, insurance and reinsurance companies operating in the European Union will be subject to a new regulatory requirement called Solvency II, which is expected to come into effect in 2013. Solvency II, as drafted but with the consultation still on-going, requires insurance and reinsurance companies to hold sufficient capital to meet its obligations over the future 12 months with a probability of at least 99.5%. This is called Solvency Capital Requirement (SCR). The regulation will provide some standard formulas for assessing the 1 in 200 year event, or the insurer can build their own ‘internal model’ to assess the risks in their business. But whether an internal model or the standard formula provided by the regulator is used to calculate the capital requirements, these models need to include future longevity assumptions to satisfy these capital requirements.  

    Conclusion

    The market for financial products associated with longevity risk is going through a period of substantial change, where the modelling of longevity risk will be even more important in the future. Although the new regulatory requirements and product developments will provide challenges there are also opportunities for improvements in the modelling of longevity risk. To meet the future demands actuaries will need to make full use of a wide range of data and modelling techniques to ensure more accurate assessment and modelling of longevity risk.