Application Of Data Mining Techniques In The Analysis Of Banking Customers

With the development of society and technology, business banking monopoly has been broken, especially in recent years, China's banks have took undergone enormous in terms of the scale of development and market-oriented process, while also speeding up the banking information and digitization. Bank's business model is being transformed from passive service to active service, it is has been in customer-centric business era. And customer resources have become a bank's most valuable asset. Banks manage customers by building a database of customer information. However, with more and more abundant data of the bank, the mass data are described as "the data are abundant, but the information is lacking". As a result, the large-scale database turned to "the data grave". Because the bank manager lacks the tool which can extract the value information from mass data. Many important decisions of the bank are not based on data with rich information in the database, but on manager's intuition. Data mining is feasible as it has access to huge amounts of information. Data mining could extract key information to provide guidance to strategic decision and operating management. Therefore, this article was analyzed on customer segmentation, customers churn and customer credit rating with the customer data of bank based on data mining.Based on the related literature research, this article describes the concept of data mining, analysis the common techniques of data mining, such as decision tree, association rule, cluster analysis, regression analysis, neural network. The article applied data mining techniques to customer's analysis with CRISP-DM and SPPS Modeler data mining tool. And select the most effective and accurate data mining model with comparison method. Positive researches indicated that the prediction correct rate of sample is higher, so research approach is effective and reasonable.This paper focuses on the use of data mining technology for bank customers to analyze research information, mining customer spending trends and consumption patterns, in-depth and comprehensive mining the law and reason of customer information valuable, draw the appropriate conclusions, and propose targeted the management advice, so that banks can truly understand the needs of customers, make the right judgment to the value of customer, can correct to keep the old customers, develop new customers, make prediction to the customer's credit risk, and thus improve the overall effectiveness of the banks can be in a good position in the fierce market competition.