Applying Data Mining Technology for Insurance Rate Making: An Example of Automobile Insurance
AbstractIn this paper we discuss the use of modern data mining (DM) methods to design risk-based insurance premiums for motor vehicles. Our objective is to predict the likelihood and expected value of future claims for each insured based on a myriad of attributes available in the database on the customers and their peers. The model results may then be used for underwriting and for rate making. We employ a two-stage approach, involving a survival analysis model and a linear regression model, to estimate the risk level of each customer and the proneness to file a claim. The study was performed on actual data set obtained from a small insurance company. We demonstrate our ability to discover new underwriting parameters, build accurate predictive models and to distinguish between distinct groups of policies. The new method creates a new ordering of the policies where the most risky people were, on the average, 12 times more expensive than the least risky people. The importance of the study is not in the particular results, which are specific for the particular company and its environment, but rather in the demonstration of the general ability to use data mining for insurance rate making purposes, and in the original use of the concept of survival analysis and the concept of mean time between claims for this purpose.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by De Gruyter in its journal Asia-Pacific Journal of Risk and Insurance.
Volume (Year): 2 (2007)
Issue (Month): 1 (May)
Contact details of provider:
Web page: http://www.degruyter.com
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Golla).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.