One of the advantages of using analytics for P&C insurance is the ability to more effectively create a rating structure or to better price premiums for a given policy. But challenges remain on how to build the best tools in this area, particularly in the development of multivariate analysis (MVA) tools.
In other disciplines, such as marketing and credit card risk, the use of predictive analytics and associated MVA tools has become common. But implementation challenges exist within the P&C industry, despite the fact that actuaries all have strong mathematical backgrounds. Why the disconnect?
The answer to this dilemma is not the mathematics being employed, but rather a lack of knowledge in using the data in the right manner to take full advantage of these techniques. In order to fully leverage the results of any MVA tool, hundreds of thousands of individual policy records with several hundred variables per policy need to be created, which is the core skill set of the data miner.
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