Research On Case Study Of Borrower’s Transactional Behaviors In P2P Lending In China

The role of small and micro enterprises in Chinese economic development have become increasingly prominent, but the difficult financing problem has not been effectively addressed. With the deepening of Internet technology, a new financing model--P2 P lending came into being, which, to a certain extent, ease the pressure on the financial needs of small and micro enterprises and self-employed households. The advantage of free geographical restrictions and fast trading make it soon popular in public. In 2007,the first P2 P lending company was founded, in 2009,there is only 9, at the end of 2012,the number of P2 P lending platform became more than 200.With the explosive growth, at the end of 2014, the number of P2 P lending platform is over 1600 and the cumulative turnover are more than 200 billion RMB.Despite the rapid development of P2 P lending, its success rate of lending platform is still relatively low, and even the average success rate of nation’s largest network lending platform is only about 10%, while the success rate of our lending platform does not exceed 20%.Therefore,the purpose of this paper is to analyze the problem and try to come up with effective solutions.In P2 P network lending process, there are involving borrowers, lenders and network platform. The network platform as the intermediary provides a place for information exchange and flow of funds for borrowers and lenders. Borrowers on the platform open their personal relevant information and publish loan information, while loan borrowers who browse different information then invest within their own acceptable risk range. The purpose of the platform is to match the needs of the two sides to promote bilateral trade and thus charge a fee and service charges. Because the network platform has relatively perfect information of borrowers, the data for the study were abundant, therefore, only the borrower transactions were discussed in this research, while the trading behavior of lenders do not involved. This paper selects the first domestic P2 P networks pat loan lending company as an example to explore the borrower transactions, and use its historical transaction data on the site to Empirical Analysis of loan borrowers success factors and financing costs(loan interest rate) factors, then based on the empirical results, this paper made specific recommendations. This study may improve the domestic research on P2 P networks borrowing, and make the lending platform improve the efficiency of the network and promote the efficient of P2 P lending network development.In the empirical part, we use crawlers to crawl from the Paipaidai site lists information of all borrowers in 2014, and 3371 were screened out as data analysis. First, base on the cluster analysis to borrower’s behavior on platform, we found that the same credit rating of borrowers has different trading behavior and different credit rating of the borrower has some similarities in the transaction process. Secondly, By using the Logistic regression model to analyze the borrowers borrowing factors we found that the borrower’s age, borrowing credit, the number of successful loan, loan interest, whether the safety standard, the number of bidders and other factors on loan success rate was significantly influenced. Thirdly, By using the multiple linear regression model to analyze the factors on affecting borrowers borrowing rates, we get the results which showed that the borrower’s credit rating and the number of successful borrowers have significant impact on the borrowing rates. Finally, based on the results of the empirical analysis we made some suggestions. In terms of user training, the Paipaidai company should dig out more high-quality user and increase user stickiness. In terms of credit system construction, it is recommended to strengthen the certification assessment on the factors that affect the success rate of borrowing and borrowing rates, while the impact was not significant should be reduced in certification. That can improve platform operational efficiency and make the platform more convenient to the public.