Study On Credit Evaluation Of Network Operator’s Customer

Credit management and control has become one of the important works of modern enterprise internal management. With Network Operators market scale expansion, Network Operators increasingly fierce competition in the market, followed by the customer’s bad debt and "default" behavior also seems to be common occurrence. Therefore, the Customer Credit Risk analysis and evaluation of Network Operators are very necessary. A Customer Credit evaluation research of French Network Operator’s Customer, to rationally divided Customer Credit Risk rating, effective risk control with the customers, improve the enterprise’s ability to avoid credit risk, finally for our country Network Operators to carry out risk management to improve using for reference.This article first through considering customer defaults, customer profiles and a variety of factors such as consumer credit information, application of Logistic Regression analysis method to establish Customer Credit evaluation model, on the basis of the model parameter estimation results to build the Scorecard, forming a set of quantitative criteria, the customer is divided into five types of credit rating. Then, based on the analysis of Logistic Regression and the Scorecard, using the method of system Clustering Analysis and on different levels of five types of customer portraits for further subdivided.The work of this paper can help enterprises to form a reasonable credit evaluation system. Scorecard build will make enterprises can forecast customer potential default level, the implementation of customer service and reasonable order, improve the level of profit. Through customer segmentation, will help the enterprise marketing personnel according to the customer’s different property distribution, accurately grasp the characteristics of customer group and personalized requirements, carry out reasonable marketing plan, and improve the operational efficiency of the enterprise. Enable enterprises to make the comprehensive quantitative judgment of Customer Credit Risk, according to credit rating for the client to take pertinent measures to guard against the customer’s credit crisis.By using SAS software, this paper carried out the Logistic Regression analysis, the accuracy of the model is 91.3%, with a higher prediction precision for the enterprise to improve a set of accurate credit evaluation standard. At the same time, in the process of building model IV value method is used to once again explained variable selection, greatly improve the efficiency and accuracy of the model. To further in-depth analysis of Customer Credit, this article, on the basis of the Logistic Regression analysis was carried out by Cluster Analysis, intuitive clearly on the customer’s credit rating.