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Software projects - the sting in the tail

In an earlier blog I looked at the arguments in favour of buying in specialist software, rather than trying to build it yourself.  Of course, as someone whose business is providing software services for mortality and longevity work, I am somewhat partisan.  To balance things out, I wrote a follow-up blog on when it makes sense - even for us - to source external software comp

Written by: Stephen RichardsTags: Filter information matrix by tag: software

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.

Written by: Angus MacdonaldTags: Filter information matrix by tag: right-censoring

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).

Written by: Stephen RichardsTags: Filter information matrix by tag: Nelson-Aalen, Filter information matrix by tag: confidence intervals, Filter information matrix by tag: deduplication

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 annuities, as shown in Fig

Written by: Stephen RichardsTags: Filter information matrix by tag: Nelson-Aalen, Filter information matrix by tag: annuities

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.

Written by: Stephen RichardsTags: Filter information matrix by tag: OBNR, Filter information matrix by tag: deduplication, Filter information matrix by tag: geodemographics, Filter information matrix by tag: survival analysis

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?'"

Written by: Angus Macdonald

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 November 20

Written by: Stephen RichardsTags: Filter information matrix by tag: mortality projections, Filter information matrix by tag: coronavirus, Filter information matrix by tag: outliers

Golden Brown

Increasing Longevity through transfusions of young blood seems potentially exploitative. Other substances, not so much...
Written by: Gavin RitchieTags: Filter information matrix by tag: microbiome, Filter information matrix by tag: immunotherapy

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.

Written by: Angus MacdonaldTags: Filter information matrix by tag: data representation, Filter information matrix by tag: sample paths, Filter information matrix by tag: counting process, Filter information matrix by tag: marked point processes

Testing Times (version 2.8.7)

We have the next release, version 2.8.7 of Longevitas and the Projections Toolkit up on the ramp. So what exactly is in there?
Written by: Jenny HalpinTags: Filter information matrix by tag: Testing