ZOPA, the earliest platform of P2P credit network, was born in Britain in 2005. PROSPER, founded in 2006, the most active credit is generally used for online auction on loan on the network platform. Foreign studies in this field are systematic and deep and will be introduced by the following two points.

1.1 Theoretic Study

When doing transactions on the credit network market, both sides could not have direct communications which will inevitably produce the information a symmetry problems, what’s more, most of the P2P network platform adopted unsecured credit loans, all contributing to huge credit risk which results in the much higher loan interests than that in traditional loans (Klafft, 2008). P2P is similar to social platform, and it is more convenient to acquire the information of the social capital quality compared to traditional financial market, namely soft information from borrowers. Robert and Benjamin (2010) have found that we can choose high quality borrowers by integrating the credit systems in the community to get access to borrowers’ reputation, personal credits, etc. This way can effectively reduce the probability of moral risk and adverse selection. Although the cost is relatively high, it is still lower compared to the traditional lending patterns.

Research on the role of social networks in lending activities found that people who have rich social network resources are more likely to get a lower loan interest, and loan default rates are lower (Lin, 2010). Studies from Freedman and Jin and Everett (2008) show that loan default rates will be lower when the lenders have connections with borrowers in real life like alumni and colleagues. It is obvious that social network in real life have a great impact on the credit activity. The other aspect concerned with Regulatory, if the supervision on the borrowers is directly operated by the lenders, the cost will be very high, on the contrary, intermediary institutions like banks with its technology and scale advantage to make the post-supervision can greatly reduced the costs(Diamond, 1984). Since the financial intermediaries are just regulators, not lenders, whether the financial intermediaries will conscientiously do the regulation job will also be a problem. In refer to this question, the study from Blackwell, Winters (1997) shows that lending rates is positively related with bank supervision and negatively related with intimacy between companies and banks. Therefore, borrowers can effectively lower the cost of financing through relationship. Besides, in consideration of for-profit funds, if financial intermediaries fail to judge the repayment ability and credit level of some borrowers, higher borrowing rates will be implemented and will mistakenly make some high quality borrowers unable to raise funds, or receive money at higher interest rates, which pushed up lending rates, results in the problem of credit rationing (de Aghion & Gollier, 2000).

1.2  Empirical Study

In the lending market, information is the key factor to successful financing (Stiglitz, 1981).The  information we can observe and get from the internet is called “Hard Information”. Since investors can not have direct communications with the borrowers when investing on the net credit platform, decisions will be made through direct information, including the borrowers’ condition:

debt/income ratio, credit rating, credit card number and so on, also including the characteristics of the borrowing information: loan amount, interest rate, purpose, etc. The main method of gathering information above is to develop the open data from net credit platform like ZOPA, PROSPER, Lending club. A large number of research show that the “hard information” will have significant  impact on the result of the loan (borrowing rate, the number of bids and borrowing rates),it even has certain relevance with the borrower’s repayment quality (default). The conclusions vary according to different emphasis of research.

Klafft (2008) analyzed  the data on  the Prosper and concluded that the credit rating has greatest impact on the loan interest rates, and the impact is greater than the borrower’s debt-to-income ratio. Other information (such as bank account information, whether their own property or not, etc.) is almost irrelevant with Borrowing rates. Surprisingly, however, when loan results is taken as dependent variable, The presence or absence of the borrower’s bank account crowd out the borrower’s credit rating to become the most important factor. Since the borrower’s credit rating is a more complex variable containing bank account information, the conclusion above is hard to explain. Klafft (2008) also pointed out that borrowers with poor credit rating that could not make a loan in the traditional financial institutions can hardly borrow money through P2P.His analysis of the data shows that HR list of those borrowers who have worst credit rating accounted for 57.4% of all loan list on Prosper, but only 5.5% of them get the loans successfully, on the contrary, the successful borrowing rate of the borrowers with a AA credit rating is as high as 54%.