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This paper sets out Isio’s thoughts on future mortality following the publication of the latest actuarial mortality projection model, CMI 2025.

The Continuous Mortality Investigation (‘CMI’) is the body that develops modelling tools that are used by pensions actuaries in setting assumptions. Whilst there are two main building blocks to the mortality assumptions that actuaries use, this paper focuses on the projection assumption, set using the CMI model to predict the future path of mortality improvements. The CMI has said that the CMI 2025 model is a ‘business as usual’ update following wider changes made to the CMI 2024 model last year.

2025 was a record low year for UK mortality – following on from 2024’s experience of similarly low mortality. The experience since 2024 could signify a “new normal” or could reflect the expected result of mortality events in more vulnerable cohorts being brought forwards by the pandemic. This makes forecasting long-term trends based on this near-term data particularly challenging, and even more so when coupled with the fact that trends in mortality continued to be different across different age groups. At the time of writing, 2026 mortality experience is looking similarly low to that of 2025.

Whilst recent experience has been positive, there still remain the significant headwinds that mean our view tends towards the hypothesis that recent experience results from mortality events being brought forwards by the pandemic, rather than this being a new normal of consistent improvement bedding in. In addition, wider global factors remain at play, strains and inflationary pressures on the economy are increasing, and the NHS funding and capacity issues have not appeared to be solved. However, longevity tailwinds are also present through emerging medicines and medical advances – meaning forecasting mortality rates remains just as challenging as ever.

The main purpose of this paper is to set out a central best estimate view suitable for accounting purposes, amongst others. We explain our views in further detail in the paper.

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