Credit transactions between enterprises are getting increasingly common, and credit sale (Credit Sale, CS) risk management has become an important part of modern business activity and management. Currently, the accounts in arrears between enterprises have become a very serious problem, due to the imperfect internal management mechanism and lack of effective credit sale risk management system. In order to make products quickly enter market to win time and achieve scale effect, credit sale has inevitably become the choice of emerging market enterprise. Based on this, we carry out the study to discuss the credit sale risk of emerging market enterprise. Firstly, we do the costs analysis of product credit sale of the emerging market enterprise, and establish a theoretical relationship between the accounts receivable holdings and the credit costs. Secondly, the general analytical expression of optimal accounts receivable holdings and the lowest credit costs is deduced; further, the concept and calculation method of risk-adjusted return on credit sale is proposed. Finally, based on the characteristics of emerging markets, we propose the concept of “credit sale risk capital”, which should be reserved in order to avoid the unexpected losses caused by the product credit sale risk, thus making the return of credit sale match the risk of emerging market enterprise.

Information asymmetry makes network transaction at risk, and trust is the foundation of network transactions. Under network transactions environment, the trust evaluation is important to predict the trust object’s credit risk. Therefore, on the basis of analyzing the influential factors of trust, we proposed an improved trust evaluation model based on cloud model; further, credit risk evaluation methodology was proposed based on the trust evaluation model. Taking C2C as an example to do the numerical experiment, results show that the trust evaluation model and credit risk evaluation method proposed in this paper, can make a reasonable evaluation and interpretation of the credit risk under network transactions.

Banks are not able to identify every cardholder's credit information in their credit card business, so they can't control the credit risk effectively. To solve this problem, this paper establishes a complex credit risk system of credit card business based on multi-agent. Simulation of different initial state of cardholders and types of cardholder in the credit card market is carried out. Through the analysis of the simulation, we believe that more attention should be paid to the "overdrafts contingency" cardholders by banks, in order to control the credit risk better and develop the credit card business.

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Personal credit scoring has played an extremely important role in the credit risk management of commercial banks. Specially, application scoring provides an important basis for the approval of customers’ credit application for the first time. In this paper, firstly, the classification of the personal credit scoring is sorted out and the definition of application scoring is given; then T test method is used to do the indicators selection; further, the author establishes the static application scoring model based on the data mining methods of Logistic regression and MCLP. The results show that among the methods that used in the application scoring, the effect of MCLP is better and it's more suitable for commercial application and promotion.

With the rapidly development of the market economy in our country, the incomes and the consumption level of both urban and residents have increased steadily. While many commercials banks have made consume loan market a strategic priority, so the research on personal credit risk has become a hot issue. Because personal credit risk management is of very importance and urgency, this paper first briefly summarizes about the concept of the personal credit risk, and then summarizes about the personal credit risks researching methods and models both home and abroad; finally, some problems of the existed researches are summarized and further research directions are suggested.

Individual credit risk evaluation has played an extremely important role in the credit risk management of commercial banks. Firstly, through Logistic regression, this paper selects and determines the clustering factors. Then the bilateral clustering structure is proposed. Based on the clustering structure, we do clustering to the test samples, and distinguish the individual credit risk as well. Finally, we use the ROC method to test the proposed model and Logistic regression model. The results of comparison show that the discrimination method of individual credit risk based on bilateral clustering can better identify the risk.