Implementation Of Credit Risk Management System Based On Data Mining

The constant adjustment of China's rural economic policies is both an opportunity and a challenge for rural commercial Banks.It's urgent to improve the technologies of information management in that the credit business is the key of rural commercial bank.Therefore,it will not only help rural commercial Banks to control credit risk,but also enhance the core competitiveness of enterprises by the way of establishing of a credit risk management system which based on data warehouse,on-line analysis,data mining and other advanced application analysis technologies.In this research,credit risk control theory and data mining technology are combined to study the credit risk management system applicable to rural commercial Banks in China.The main work of this research is summarized as follows:(1)This paper introduces the basic theory of data mining and the general process of data warehouse modeling,Analyze the significance of data mining in credit risk management in view of the limitations of current credit risk management;(2)The credit risk management system needs analysis.First introduces the system design concept,then analyzes the system feasibility from three aspects of economy,law and technology,then from customer information management,credit business management,interest management,credit officer management,system management and query statistics.Six aspects are analyzed for business process requirements,and finally the data flow analysis is performed on the system.(3)Design credit risk management system.Firstly,introduce the system overall architecture design via stating the two aspects of C/S structure and overall architecture of the system.Secondly,the system overall structure design is clarified as the customer information management,credit management,interest management,credit management,system management and query statistics.Thirdly,the data warehouse system is layout by conceptual structure and physical design;(4)Applying data mining technology to the credit risk management system,using the association rule Apriori algorithm to analyze the relationship between credit application,review and approval,and credit rating,not only can improve the success rate of credit product matching,realize customer diversion,but also help expand customers.Resources,and through risk quantification management,credit managers can control credit risk at the source.Using the decision tree algorithm C4.5 to predict the customer credit rating,credit managers can implement customer classification management for different credit rating customers,which helps to improve post-lending management efficiency.The system can promptly alert credit risk to help credit managers adjust bad loans.Receiving strategy;(5)Complete the deployment of all design functions and test the system functions dynamically.The system will be put into Lianshui rural commercial bank for deployment and trial operation after the test reaches the standard.During the trial operation of the system,the expected target can be basically achieved,which is applicable to the actual working environment of rural commercial bank and has strong reliability.In addition,the low coupling degree between the modules of the system is helpful for the later system function expansion.