Life expectancy will reduce by four months for a 65 year-old man, and six months for a woman of the same age, as a result of a new mortality projection model released yesterday, according to Willis Towers Watson (WLTW).
The CMI, owned by the Institute and Faculty of Actuaries, updated the previous model to include mortality data up to and including 2016, as a starting point for projecting life expectancy.
By doing this, assumptions used by many pension schemes and insurance companies are likely to fail, although schemes that use the new CMI_2016 model could see their liabilities cut.
WLTW senior consultant, Stephen Caine, said: "Until recently, mortality rates in the UK were falling at an impressive pace. Since 2011, these improvements have stalled.
"An unprecedented uptick in the number of people dying in the UK each year has led to the CMI's latest model revising down the projection of how long we will live.
"For some schemes about to embark on new funding negotiations, adopting CMI_2016 could cut life expectancy, and represent a reduction in liabilities of up to 2%."
How successive updates to the CMI model have reduced life expectancy is shown below:
Although improving mortality has slowed in recent years, the CMI predict life expectancy to continue to progress, but are uncertain whether it will be at a slower rate compared with the first decade of this century.
"CMI_2016 has thrown more light on some very interesting trends - namely that, in recent years, the rate at which mortality is improving has been slower than in the first decade of this century," CMI executive committee chair, James Tair, said.
"Although it is highly likely that mortality will continue to improve, there is significant uncertainty as to whether this recent slowing in the rate of improvement will continue.
"The slowing raises important questions about contributing factors. Indeed, our analysis of pensions data implies that the causes could be more complex and stratified than the pure life expectancy figures, that only consider population data in aggregate alone, would suggest."
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