Application Of Data Mining Technology In Domestic Field Of Insurance

With the insurance system reform development and our entrance of WTO and comprehensive entrance of external insurance companies, the competition of insurance operation is becoming fiercer. Compared with other industry, insurance operation has more data about customer. Enterprises that can mine and analyze the knowledge contained in the data correctly will drop rate of claim offer insurance product and to customer better and find more opportunities, thus win the competition. Our domestic research on this field is still at infancy, external research has already been superior to mine greatly. So It is vital and valuable to investigate the application of data mining technology in domestic insurance industry. Firstly, we analyze application of data mining technology in domestic insurance and introduce the theory of data mining technology. Secondly, We lay stress on the selection and design of the model of data mining in insurance industry. Six data mining pattern of domestic insurance operation are putted forward: Customer Segmentation, Customer Churn Analysis, Market Basket and Cross-sell Analysis, Customer Satisfaction Analysis, Customer fidelity Analysis, Customer responsibility Analysis on foreign research and the domestic practice needs. Medical insurance is given as a demonstration to develop a customer RC segment model,individual and group claim character model of medical insurance operation and make use of Clementine data mining tools of SPSS, establish the model and then verify and appraise it.