Design And Implementation Of The Bank Customer Management System Based On Data Warehouse

With the accelerating entry into the WTO,more and more foreign banks enter into the Chinese Market. It has a certain impact on the domestic banking industry. At the same time, with the rapid development of our economy, the society has put forward new demand to the traditional banking business. Therefore, how to attract customers to expand their business areas, avoid risks in order to reduce non-performing loans and improve the quality of service to the continue the vitality of enterprises, Is a key to sustain bank‘s development.Combined with the bank’s own vast historical data of customer transaction, we construct a central data platform by the data warehouse(DW), and apply data mining technologies; we develop valuable user information or trading rules. It will provide efficient and scientific support for bank management and decision.Based on the actual business need of Agricultural Bank, by survey and research status of related fields, the thesis apply the DW into customer management system of Agricultural Bank, construct centralized data platform by DW, complete the data processing by ETL and Mining and analysis with DM, in order to evaluate the user with scientific approaches and then provides the risk analysis and post loan management, it can effectively detect the target customers, and then market personalized marketing project, assess achievement comprehensively, then Perfect reward mechanism and promote the development of enterprises.This thesis presents the overall architecture of agricultural bank’s customer management system.And with combination of the actual needs from system, we give ETL design of front-end application and data warehouse. By constructing the logic model and physics model of family system, we achieve a family system based on DW and analyze data by DM. By testing, the customer management system can manage and analysis risk about customer data succinctly. Besides, it can explore marketing cues and trading rules efficiently. Lastly, it can provide scientific data support for bank’s operational decisions.