Data from insurance portfolios and pension schemes lend themselves particularly well to the application of survival models. In addition to the traditional actuarial risk-rating factors of age, gender and policy size, we find that using geodemographic models based on postcode provides a major boost in explaining risk variation. Geodemographic models can be better than models based on pension size in explaining socio-economic variation, but a model using both is usually better still. Models acknowledging heterogeneity tend to fit better than models which do not. Finally, bootstrapping techniques can be used to test the financial applicability of a model, while weighting the model fit can be used to address concentration risk.
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