The Determinants Of Default Risk Of Chinese P2P Lending

In recent years, with the continuous progress of Internet Finance, P2 P network lending have developed rapidly. 2P(Peer-to-Peer) lending refers to direct loans between investors and creditors through the Internet platform, which does not depend on financial institutions as intermediaries. through the Internet platform P2 P networks have a low threshold of borrowing, wide coverage, and convenient transaction procedures, a small amount of the transaction, short-term borrowings and other characteristics.. P2 P networks are main consist of simple intermediary lending,complex and non-profit welfare agency operating modes.China’s first P2 P network lending platform was founded in 2007, followed by P2 P net loan platform showing growth spurt in our country, according to incomplete statistics, as of November 2014 the total number of P2 P net loan platform reached1540, trading volume has gone up 2014 annual total transaction volume is expected to break 250 billion yuan, the average monthly yield up to 18.72%. But behind the gorgeous data, my P2 P net loan platform, there are some problems: the homogenization more serious, vicious competition occurs; violations gradually increased net loan platform, or even a network under the banner of borrowing to raise funds fraud phenomenon; the case the borrower defaults are not uncommon. Domestic and foreign scholars conduct research on P2 P loans, loan defaults in the experience of other factors, the discovery borrowing rates, amount, duration; the borrower’s credit status; the basic situation of individual borrowers these three factors lending to P2 P networks late possibilities have significant influence.In this paper, 1124 successful subjects are chosen as empirical samples.The study found that borrowers fundamentals of hard information factors, the soft information of these three factors relevant information on the possibility of loans overdue P2 P networks have a significant impact. From the regression results, the borrowing rate(r)and credit rating(risk level) degree of influence on the risk of default is large,although other factors have also been significant regression results, but compared to the degree of influence of these two factors to be weak some. Because borrowing rate(r) of the maximum impact of default, we further analyze whether the same borrowing rate would mean the same risk of default. From the goodness of fit of the regressionresults point of view, after adding other factors significantly improved the goodness of fit, indicating that despite the borrowing rate(r) of the maximum impact of default,but this factor does not fully explain the P2 P network platform loans default risk.Therefore, the impact of other factors on the risk of default prediction function still can not be ignored.Based on the current development of P2 P networks and lending issues and the results of empirical analysis, we found the factors that affect the borrower defaults.Then, we proposed the prevention and control of the network of loan default P2 P networks countermeasures to promote our country better and faster lending P2 P networks development of.