Stephen Richards

Mortality statistics occasionally make the news, usually with some eye-catching statements.  Here is a recent example from the BBC: "Data from life insurance companies suggests that in the fifth and sixth decades of life you are less likely to die over the coming year than at any other time in your life."

When an insurer or reinsurer takes on a new insurance risk, there are two things of special interest: the best estimate of the risk and the tail risk.

In just over one year's time, insurers throughout the European Union will be prohibited from using a person's gender to price insurance risks.

Late last year I drew up a table of actuarial terms and their translation for statisticians.  I had thought that it was a uniquely actuarial trait to use different names compared to other disciplines.  It turns out that statisticians are almost as guilty.

In an earlier post we looked at how to create a proxy for ill-health early retirements based on age at commencement.  This is an example of dealing with missing data — we infer a useful proxy to replace the lost or missing health status at retirement.

In an earlier posting we looked at several examples where a pension scheme dominates the picture of the company's finances and value.

Insurers need to have internal models for their major risks. Indeed, both the Individual Capital Assessment (ICA) regime in the UK and the pending Solvency II rules in the EU demand that insurers have good models for their risks.

In previous posts we have looked at problems with the quality and reliability of cause-of-death data and a list of hurdles for mortality projections based on such data.  One other issue is that of detail.

Annuities are a good example of the cornerstone of actuarial work: discounting future probabilities of payment to allow for the time value of money.  Low interest rates have had major consequences for savers looking for income in retirement, but they are also one reason behind renewed actuarial focus on longevity in recent years.