Insurance Acquisition System Design Based On Data Mining

With the rapid development of the domestic economy, Insurance is playing an important role in economic activitys. More and more companies engage in insurance agency these years, which lead to fierce competition. Today, making decision only by instinct can’t adapt to the new challenges on the business.Insurance agency began in 1990s, up to now, the industry has made great development. Compared with the international, the insurance agency in China is’late start’, but our standard is high. From the beginning of the industry, we used the computer and database to manage business activities, at the same time, we established the financial system, insurance management system, insurance policy management system, insurance claims system, insurance customer management system, etc, which has accumulated a lot of business data. Through finding the rules hidden in them, we are able to quantify these rules to make models of the insurance agency, they will not only provide useful quantitative basis, but also provide the best descision and strategy for the insurance company to find VIP clients, recommend business, keep good relationships with clients and prevent risk, etc. All these can effectively reduce the decision risk and enhance the competitive force of a company.This papermainly work as follows:1. Explore the insurance industry related data.It lays a good foundation of application of the data mining technology in the insurance industry by counting, visualizing and analysing (OLAP) the accept insurance data.2. The data mining technology application in the insurance industry.(1) Put forward the customer classification model based on classification decision tree to solve the problem of customer classification which only relies on human.(2) Put forward the combination recommended model based on the FP-growth algorithm of the association rule to solve the problem that system can’t provide the combination recommended as aids.(3) Put forward the policy trading model based on DBSCAN algorithm to solve the problem that policymakers can’t analyse the actual product purchasers and the agents can’t inquire purchaser behaviours to choose right products for customers.3. Technology implementation above three models. First, setting the database from the accumulated data. Second, using the data mining technology and algorithm to make models. Third, applying to the predictive parsing of the business activities in the insurance system and support quantitative basis for decisions.With the development of economy and the change of market, revising the original models and exploring the new models own a high application value for business services.