Research On Application Credit Evaluation Model Of Credit Card Based On Data Mining Technology

In recent years, credit card business developed rapidly since the raise of income and the change of consumer's habit. The credit business becomes the core business which call cause plenty of customer and considerable profit for banks. The credit card market competes intensely day by day, But highly competition causes limits of people who apply for credit cards in commercial banks tend to loosely and they neglect risk management and control. Development of the credit card industry has brought the huge opportunity for our country's banks. At the same time, the commercial banks are facing unprecedented challenges. Whether or not it has a strong ability of balancing risks and benefits is the key to success for our banks. It is to be urgent to enhance commercial bank's credit card risk management ability. Advanced data mining techniques can mine some unknown and valuable role from the mass data of credit card. It is no doubt to provide a strong technical support for credit card credit—risk management.This paper researches model on credit-risk measurement of credit card. It introduces data mining methods which ale the most advanced techniques in current data analysis into the credit—risk management of credit card. At the beginning, the paper discusses the concepts and characteristics of credit card, credit card risk and credit card risk management,and their related theories. It elaborates the theory of data mining,the basic principle of decision tree specially and their applications in credit-risk management of credit card. The essay focused on analysis for how to create a individual credit rating model for commercial bank.Based on data of personal credit system in China, analyze implicating decision tree of data mining method to real individual credit rating model and try to find some uncover rulcs.The credit rating model will be established finally.The decision tree of credit rating model based on the real database of personal credit system,followed by data gathering,extracting,previous Processing, established the individual credit rating decision tree model.From the testing result,the model can forecast high level credit client more exactly.But to the low level credit client, the result is less exactly.lt is also a tough task to the credit rating.Received or rejected this is a question. More information must be collected for the more authentic forecast result.At last I give summary about my paper, pointing out some shortage and simple research results,thinking the prospect of consumer credit loan should be good with deep research on credit risk management going on.