Information Matrix

Previously I wrote about how mortality rates by cause of death vary by deprivation index (and, by implication, socio-economic group). This substantially complicates any attempt to use cause-of-death data to make projections of mortality for annuity portfolios and defined-benefit pension schemes.

Last night an paper on the management of annuities was presented to the Faculty of Actuaries in Edinburgh.

Boxing Clever

In an earlier blog I wrote about how stochastic volatility in run-off increases with age. This applies when you exactly know (or think you know) the current and future mortality rates.

Insurers and reinsurers throughout the EU are facing up to the implementation of Solvency II, a radical overhaul of regulatory standards for insurance business.  Recently we explored how much Solvency II demands stochastic models. 

We recently discussed the ways server-based modelling software facilitates collaboration across boundaries. Another important boundary is the office wall, although what was once considered an impermeable divide between work and the rest of our lives, is nowadays all-too porous.

Most of our readers will normally associate the acronym FSA with the Financial Services Authority, the body supervising financial-services providers in the United Kingdom.  Confusingly, another UK regulator has the same acronym: the Food Standards Agency, which has nothing to do with financial services.

Creating a good model from your experience data is not always straightforward.

Solvency II is a major overhaul of the reserving rules for insurers throughout the European Union.  An important consideration for annuity writers is how it will relate to longevity trend risk.

Geodemographic profiles use addresses or postcodes to classify people into groups which are homogeneous with respect to variables like income, housing tenure and life stage.  The original purpose of geodemographic profiles was to improve targeting for marketing purposes.