The purposes for which the CMI uses the information it collects are to:
- compare the actual mortality and morbidity experience with the expected experience based on one or more published tables
- construct tables of mortality and morbidity functions based on analysis of aggregated data primarily for use by members of the actuarial profession
- make estimates of future developments in mortality and morbidity rates
- carry out other statistical investigations and research that may be useful to actuaries with regard to the conduct of long term assurance, annuity and pension business, of sickness and related insurances and of self-administered pension schemes.
The CMI uses this data to produce:
- a summary of the data and the mortality / morbidity experience of that dataset. This is returned to the relevant data contributor and it is an integral part of CMI’s data validation, to ensure we have interpreted the data appropriately; and
- an anonymised dataset, in which no individual can be identified, which is used with other relevant data for further research,
The CMI makes available the results of its research by a number of means:
- Subscribers and other Authorised Users receive an analysis of the aggregated experience of a number of data submissions, usually for a single year or for several years combined.
- The CMI also releases the results of some of these aggregated analyses and the results of other research to Subscribers and other Authorised Users in Working Papers and other documents
In certain circumstances the CMI may supply data to universities, and others, for non-commercial research
This will be done under a Research License which sets out the obligations of the researcher(s) and the CMI
All requests for data for research should include the following:
- confirmation that the terms of the Research Licence will be met
- information about what data is required
- a summary of the purpose of the data request and the intended output.
Data will only be released for research purposes where the CMI Management Committee agrees that the proposed research may provide a worthwhile addition to actuarial knowledge. The CMI reserves the right to levy a charge for the handling of such data requests.
Any data provided for research will have been de-personalised in such form that they can no longer be considered personal data or sensitive personal data within the context of the data protection legislation. In addition, any data provided will normally be aggregated so as to preserve the confidentiality of information relating to individual life offices or pension schemes.
Occasionally the CMI may make available data that indicates, via anonymous codes, the data from individual life offices or pension schemes. Such research will only normally be undertaken at the instigation of the relevant investigation committee and the Research License requires that:
- published work based on the data will not include any figures that might allow an individual life office or pension scheme to be identified; and
- a draft copy of the report will be submitted to the CMI prior to publication for approval.
If you have any questions about the CMI please email
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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.
The role of actuaries within the health sector varies considerably from one country to another, due to differences in the local evolution of health systems and the funding models for health services.
This paper outlines key frameworks for reserving validation and techniques employed. Many companies lack an embedded reserve validation framework and validation is viewed as piecemeal and unstructured. The paper outlines a case study demonstrating how successful machine learning techniques will become and then goes on to discuss implications. The paper explores common validation approaches and their role in enhancing governance and confidence.
Content will be aimed at all actuaries looking to understand the issues surrounding mental health in insurance and in particular those looking to ensure products and processes widen access for, and are most useful to, those experiencing periods of poor mental health.
The IFoA Policy Briefing 'Can we help consumers avoid running out of money in retirement' examined the benefits of blending a lifetime annuity with income drawdown. Panellists, including providers and advisers, will look at the market practicalities of taking the actuarial theory through into the core advice propositions used by IFAs and Fund Managers. They will share a number of practical issues such as investment consequences before and after retirement and the level of annuity that is appropriate and answer questions from the audience.
The IFoA is pleased to be hosting the Governor of the Bank of England, Andrew Bailey, to deliver a speech on delivering policyholder protection in insurance regulation.
The speech will be presented to an in-person audience, and simultaneously live-streamed, at 14.00 on Wednesday 1st December.
This webinar looks at the many types of biases, both conscious and unconscious and the impacts they can have in the workplace. Raising our own awareness and understanding of the issues can help us avoid the pitfalls of unconscious bias in particular. We’ve all heard the phrase ‘office banter’ but are we sure that’s how those on the receiving end perceive it and is it ok to go along with it?
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.
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.