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Life insurers fall in behind predictive modeling

By Matthew Edwards, Towers Watson

The use of predictive modelling techniques to refine the pricing of insurance risks has been standard practice in the P&C insurance sector for many years. However, life organisations have traditionally used simple one-way techniques and so, for many life insurance providers, predictive modelling techniques represent a new era of modelling. The aim of a predictive model is to derive a meaningful relationship between the modelled event (for instance, death or surrender) and various explanatory factors (for example, age, policy duration, or postcode group).

This provides enhanced understanding of the underlying risk drivers, and is a vital input in pricing business in competitive sectors – especially where competitors are pricing on a segmented basis (for example, pricing annuities according to postcode or fund size).

Traditional approaches have attempted to explain the modelled event using a very small number of explanatory variables. But they frequently fail to allow for the effect of correlations in the data, and this can often lead to material misunderstanding (and hence mispricing) of the relevant risks.

In response, many life insurers have recently started to use ‘multi-dimensional’ predictive...Read More

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AE for Group Income Protection: Dutch Inspiration

In the second article of a landmark series to champion AE for GIP, Canada Life’s Paul Avis discusses with Editor Ellie Burns how we need to look to the success of the Netherlands, and how the Dutch model could be the answer to a stagnating product at risk of extinction, as well as the answer to a complex welfare system which is repeatedly missing the mark.

EB: In the first article you established that GIP was a product you were focused on as it is at risk of stagnating and extinction. Is AE for GIP really on the table?

PA: I feel like a lone voice, or perhaps I am a classic marketer who believes in product lifecycle-introduction, grow...Read More

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