Risk Based Comparison between two data mining methods in segmentation of car insurance customers (Case Study: Mellat Insurance Company)

Due to the sharp rise of the information technology (IT), the amount of data stored in databases is dramatically on the rise. Analyzing the stored data and converting it to information and knowledge which is applicable in organizations requires powerful instruments. As with other economic sectors, recognizing and attracting low-risk and profitable customers are of high significance for insurance industry. Car insurance is one of the most important insurance branches which accounts for a great deal of portfolio of insurance industry. Risk segmentation of policyholders on the basis of observable features can help insurance companies to reduce loss, raise the rate of insurance coverage, and prevent them from making an inappropriate choice in the insurance market. In this study, the segmentation of comprehensive car insurance customers on the basis of risk was selected through self-organizing map and K-means. At first, the effective factors on the risk of policyholders are identified. Then, the insurance policyholders are segmented using the proposed SOM and K-means. Customers’ characteristics in every cluster are identified. Finally, the two methods compared with each other. The advantages and disadvantages of them illustrated.