The Application Of Data Mining Technology In The Analysis Of Credit Card Customers

With the development of finance economy, the domestic credit card market has gradually grown up and been more and more prosperous. In recent years, more and more people use credit card to make payments and the credit card has become the most popular non-cash payment instruments in china. The domestic credit card market is highly competitive with the seizing of market share by local commercial banks. As foreign banks which owned advanced decision support system paced in, the competition obviously increased. In order to succeed and obtain a larger market share in the fierce competition, it is important for local commercial banks to offer innovative services and provide a better relationship management to tackle the challenge. However, credit card, the products of standardization, it is hard to create a new feature different from the competitors and difficult to imitate. If the local commercial banks want to stand out in the credit card market, they must seize differentiation of the credit card products. The differentiation of credit card products mainly reflected in the differences of credit card customers. As a result, the banks need to classify the credit card customers with the advanced data mining technology, and then take different measures to target different customers, such as the different marketing, service, risk control measures etc. At the same time, the banking system has accumulated a large scale of business data which has provided a basis for the application of data mining. The local commercial bank should seize the opportunity to apply the data mining technology to help the decision making and the providing of different service.There are two important meanings for local banks to use the data mining technology to analyze the consuming custom of credit card customers.On one hand, it is helpful in the maintaining of customer relationship and can provide useful information for risk management. On the other hand, based on the information provided by data mining, the management can make a more precise decision to innovate the product, to provide personality service and enhance competitiveness of credit card.Under current situation stated above, the writer attempt to do research on the data mining technology application in the analysis of credit card customer. The emphasis of the research is to gain an ideal analysis model and analysis result based on the analysis of business data from one major commercial bank through the data mining technology.The writer has completed the following steps to finish the research, using some major tools in data mining area such as SQL SERVER2005, SAS/Enterprise Miner, etc.Stepl Read abundance of books related to the data mining field and got a deep understanding of the data mining theory.Step2Completed the data preprocessing, including the data extract, data clean, data transform and data load.Step3Completed the clustering analysis of customer data.Step4Set up the customer classification model based on the decision tree.Step5Summarized the success and shortcomings in the research and provided some suggest about the future research.At last, as the deep development and application of data mining technology in the banking business, the commercial banks gradually transfer the emphasis of business to the customer services. Also, the data mining technology can help in the field of risk management and profit making, through which the commercial banks can improve its core competitiveness. In recent years, the credit card business got a fast development in china. Meanwhile, the data mining technology developed a lot. Also, the writer has completed a lot of work on the data mining technology application in the credit card business analysis. The writer has given some constructive advice for the application of data mining technology in credit card business. However, due to author’s limited academic ability and the ability of analytical techniques, the depth of the theory and experimental value is restricted. The writer only hopes that this paper can contribute its modest means to the research in related fields.