Working Paper 155 was published in October 2021. This paper describes the experience of enhanced pension annuities (annuities for individuals with medical or lifestyle impairments) over the period 2011 to 2019; the results are available in accompanying spreadsheets. The paper also provides an indicative analysis of the experience of enhanced annuities in the first half of 2020, covering the “first wave” period of the Covid-19 pandemic.
The analyses show that:
- Overall, the mortality experience of the enhanced annuity dataset is considerably heavier than that of non-enhanced annuitants.
- The experience is highest at younger (pensioner) ages and is closer to non-enhanced annuitants at the older ages.
- The enhanced annuities dataset shows a similar downward trend to the non-enhanced dataset when considering mortality rates by deprivation (measured by the Index of Multiple Deprivation, or IMD), with higher mortality in more deprived deciles.
- Mortality experience, when expressed as Standardised Mortality Rates (SMRs), is substantially higher in April and May 2020, the period when the Covid-19 pandemic impacted population mortality than in the corresponding months of 2019.
The accompanying results are available in two forms:
- Summary spreadsheets, with results for 2011-2019 combined, both without improvements and with improvements from CMI_2020.
- Datasheets containing the detailed data, for example by individual age and individual calendar year.
The summary results spreadsheet provides a high-level overview of the results but does not allow users to choose an alternative base table. Users wishing to see more granular data, and to alter the comparison basis, should refer to the datasheets.
Note: this paper and the accompanying spreadsheets are available to Authorised Users only.
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This practical course is aimed at actuaries at any stage of their career who want to develop their own growth mindset and apply it to their work setting and personal or professional lifelong learning. The content of the course builds on the lecture given by Dr Helen Wright on Growth Mindset as part of the President’s 2021 Lecture series, and will be delivered over a period of 2 months, from mid-October to early December.
Actuaries need to take action now - but how? With a focus on climate change, this session will provide informed insight to enable you to improve your knowledge and understanding of the issues involved, demonstrate how it will impact advice to your clients, and highlight prospective opportunities for actuaries within pensions and wider fields.
A joint webinar from the CMI Mortality Projections and SAPS committees that will cover: recent mortality experience in the SAPS dataset and the general population; the CMI Model benchmarking survey; the MPC 2021 interim update paper; plans for CMI_2021; and initial thoughts on possible "S4" Series pensioner mortality tables.
The webinar will be presented by Cobus Daneel (Chair of Mortality Projections Committee) and Matthew Fletcher (Chair of SAPS Committee).
Pension scams have become more prevalent as a result of the pandemic, and Trustees have increased responsibilities to protect members, which means that actuaries need to be in a position to provide advice in this area. Our specialist panel will include a professional trustee, an IFA and head administrator, two of whom are members of PASA.
The Covid-19 pandemic creates a challenge for actuaries analysing experience data that includes mortality shocks. To address this we present a methodology for modelling portfolio mortality data that offers local flexibility in the time dimension. The approach permits the identification of seasonal variation, mortality shocks, and late-reported deaths. The methodology also allows actuaries to measure portfolio-specific mortality improvements. Results are given for a mature annuity portfolio in the UK
In this webinar, the authors of the 2021 Brian Hey prize winning paper present a new deep learning model called the LocalGLMnet. While deep learning models lead to very competitive regression models, often outperforming classical statistical models such as generalized linear models, the disadvantage is that deep learning solutions are difficult to interpret and explain, and variable selection is not easily possible.
The dominant underwriting approach is a mix between rule-based engines and traditional underwriting. Applications are first assessed by automated rule-based engines which typically are capable of processing only simple applications. The remaining applications are reviewed by underwriters or referred to the reinsurers. This research aims to construct predictive machine learning models for complicated applications that cannot be processed by rule-based engines.
With the Pension Schemes Act 2021 requiring a long term strategy from Trustees and sponsors, choosing a pensions endgame strategy has become even more critical. However, it is important that the endgame options available are adequately assessed before choosing one. With an ever-increasing array of creative and innovative options available, this decision may not be straightforward.