Document Type : Research Paper
Abstract
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
Keywords
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