Using Data Mining Techniques to Predict the Detriment Level of Car Insurance Customers

Abstract

Nowadays customers’ role is changed from just accepting the producers, to leading investors, producers, and even researchers and inventors. Therefore, it is necessary for organizations to identify their customers well and to make plans for them. Some statistical and machine-based learning methods are used so far. However these methods alone are not without limitations. Using various methods of data mining, this research was to eliminate those restrictions as far as possible, so that a framework for identification of car insurance customers could be provided. In fact, the purpose was to categorize the most similar customers and to estimate the amount of risk in each category, according to their characteristics. Now, using this scale (i.e. amount of risk in each category) and considering the type of customer’s policy, the level of recompense could be estimated. This criterion can be helpful to identify customers and for making insurance tariff policies. For this purpose, in insurance industry the two data mining methods were been used to estimate customers’ detriment: the decision tree and clustering. Nevertheless, the decision tree method appears to give better results, although at the same, the clustering method generates a good categorization.

Keywords

• Data mining
• insurance
• categorize
• decision tree
• clustering
• detriment