Data Mining in Insurance

17 Dec

I have been working on a project which uses data mining techniques to use predict insurance outcomes. I have leaving it for a long time to write up the resources I found, as part of the point of this blog was to diarise the stuff I found during exactly this sort of research. I think that originally I wanted to give relatively detailed summaries of these items, but I begin to realise that I am in danger of never writing them up at all.

This first pamphlet is a good high level summary of data mining techniques and how they can be applied to some general insurance problems. Handy if you need to explain concepts to a non technical person.

http://www.casact.org/pubs/forum/03wforum/03wf001.pdf

The paper below emphasizes CART across a range of insurance contexts, and like the paper below discusses hybridising CART and MARS techniques (although they are by the same authors).

http://docs.salford-systems.com/insurance4211.pdf

Below is a comprehensive study of claim size prediction using a hybrid CART/ MARS model. Interestingly, the hybridisation is achieved within a single model, rather than creating separate models within an enesmble, for example by boosting. The authors don’t address the topic of boosting at all, in fact, which possibly a more obvious approach. This presentation is in fact a more detailed look at one of examples from the paper above.

http://docs.salford-systems.com/RichardBrookes.pdf

This last is a more specific look at text mining in relation to a topic which is one of the concerns of the CT6 exam – claim prediction – but obviously using techniques not currently set for examination in the actuarial exam system.

http://actuaries.asn.au/Library/gipaper_kolyshkina0510.pdf

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