Analytics and Data Mining for Insurance Claim Risk

Richard Boire

Chapter Chapter 30 in Data Mining for Managers, 2014, pp 229-230 from Palgrave Macmillan

Abstract: Abstract The primary advantage of using analytics and data mining for P&C insurance is the ability to more effectively create a rating structure or to price premiums for a given policy. The challenge is how to build the best tools in this area, particularly how to develop multivariate analysis (MVA) tools. In other areas, such as marketing and credit card risk, the use of predictive analytics and associated multivariate analysis (MVA) tools has become commonplace. Why does this pose a challenge in the P&C industry; after all, actuaries usually have a strong background in mathematics? The answer lies not only in the mathematics being employed but in the lack of knowledge on how to use data in the right way to take full advantage of the techniques available. In order to fully leverage the results of any MVA tool, hundreds of thousands of individual policy records with several hundred variables per policy record must be created, and this is the core skill set of the data miner. The discipline of data mining is relatively new, and the training of actuaries is not fully developed in this area. Nevertheless, the academic training actuaries receive in mathematics and statistics can definitely be applied to data mining. However, the most important component of data mining, and arguably the one that is very resource-intensive, is the data environment.

Keywords: Data Mining; Data Environment; Rating Structure; Price Premium; Primary Advantage (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:pal:palchp:978-1-137-40619-4_30

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DOI: 10.1057/9781137406194_30

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