Data Mining Technology In The Bank Private Business Crm System

Since China joined WTO, foreign investment banks have entered Chinese financial market gradually. This is a new challenge to the banking management and the competition situation of traditional Chinese banks. Nowdays, customer service strategy needs to be specialized, categorized, many banks have already put into plans on implementing such strategies. As a new execution mechanism of improving the customer relationship with the banks, bank CRM has already become into the most popular tool used by banks for such business needs.It is very important to have data mining, data utilization and reusability after the banking data being processed centrally. Therefore, it is very necessary to apply data warehouse technology, as data warehouse is becoming the most important project for all banks. Accordingly, here comes another issue, the heavily increased data amount determines the huge amount of useful information that are stored in those banking database, purely replying on manually process by human being or simple data index won't be far than enough. The result of such conflict is the increasing gap between creating data and understanding the data, this is so called "rich data, poor information" situation. In order to prevent such problem from happening and reduce the cost of such investment, it ought to have a new data analyzing technology. data mining is such technology that meets the banking business requirement, it makes it possible to automatically and intelligently transform the massive amount of data into the useful information.CRM is based on data mining technology, it analyzes the banking customers behavior data, digs out the important customers data, assesses the marketing function. It is replacing the bank legacy customer information management mechanism, taking over the old data process model, smoothly solving the emerging customer service problems that most banks are facing.This project is focusing on the data mining of private banking customers, applying private banking CRM technology, introducing data warehouse, data mining, OLAP core technologies, and CRM system implementation. It analyzes the current issues of the existing banking system architecture and CRM system limitations, and it also specifically explains the system blue print and strategic integration planning, detail specific plans and goals, system infrastructure and architecture, algorithm design and implementation. It also analyzes several different methods of data mining implementation for banking CRM data, mainly focusing on the design of the decision tree categorization algorithm of the critical customers data. It also summarizes the overall result of the system design and performance assessment.