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A Type I flower by any other name
I must have been one of many students who chose maths over medicine, because I have a terrible memory, and medics have to memorize books by the kilogram. In maths, if you understand how to do something, there is nothing to remember. Right?
Up to a point. Here are three mathematical relations where the order or direction matters, that I can never remember, no matter how often I have encountered them.
Real-time decision making
In a previous blog I looked at how continuous-time methods can provide real-time management information. In that example we tracked the (almost daily) development of the mortality of two tranches of new annuities, as shown again in Figure 1.
Figure 1. Cumulative hazard, \(\hat\Lambda(t)\), for new annuities written by French insurer. Source: Richards and Macdonald (2024).
Real-time management information
The sooner you know about a problem, the sooner you can do something about it. I have written before about real-time updates to mortality estimates during shocks. However, real-time methods also have application to everyday management questions. Consider Figure 1(a), which shows a surge in new annuities in December 2014. The volume of new annuities written in that month was large enough to shift the average age of the in-force annuit
The actuarial data onion
Actuaries tasked with analysing a portfolio's mortality experience face a gap between what has happened in the outside world and the data they actually work with. The various difference levels are depicted in Figure 1.
Figure 1. The actuarial data onion.
Anglo-Saxon attitudes
Scene: A meeting room, London, c.1997. Two actuaries are contemplating a flipchart on which is displayed some mathematics, including a double integral.
Actuary 1: "That's the kind of thing a Danish actuary would understand.'"
Actuary 2: "Yes, but could they calculate a premium rate?'"
Mortality forecasting in a post-COVID world
Last week I presented at the Longevity 18 conference. My topic was on robustifying stochastic mortality models when the calibrating data contain outliers, such as caused by the COVID-19 pandemic. A copy of the presentation can be downloaded here, which is based on a paper to be presented at an IFoA sessional meeting in N
Golden Brown
No calculation without representation
You are in charge of systems programming for an insurer writing disability insurance. It is your job to write reporting modules to meet the needs of the actuaries, claims managers, accountants and so on. Where to start?
The data would seem to be a good place. I'll take it as read what kind of data the business will generate. The question is how to represent it for efficient use in our programs - something we worry about so that the user doesn't have to.