Research On P2P Lending Platform And Online Borrowers

With the help of the modern technology, a new mode of personal lending structured on the Internet (as known as online Peer-to-Peer lending) is developing significantly in recent years. Specifically, this new mode has no relationship with traditional banking system, but all depends on ordinary individuals. So far, P2P lending websites have appeared in different countries such as U.S., U.K., Canada, China, etc. However, participants in P2P lending confront the asymmetric information problem, and lenders have to take the risk of defaults or delinquent repayments. Hence most of the online platforms are attempting different ways to minimize the information asymmetric, including verification, group communication, reference, etc.This paper first summarizes the features of several successful P2P lending websites and makes comparative analysis. Concerning the quantitative analysis, we collected data from PPDai ranging from March2011to December:(1) Borrowers’behavior patternsAccording to the data collected from PPDai, this paper researches on behavior patterns of borrowers. Cluster analysis reveals that borrowers’activities and behavior patterns differ a lot and their features could not be fully represented by the’credit ratings’designed by PPDai. Some users borrow most frequently and possess the highest rate of full-fund, and their purposes of financing are usually fixed and stable. Some other borrowers with high lend score do not ask for loans quite often but their loan amount are fairly large each time, they borrow mostly for emergency use or temporary circulation inside PPDai. This result demonstrates that users mentioned above are more active and developing faster than other users, plus they possess stronger stickiness to PPDai.(2)Factor analysis on full funding and predicting models for borrowers This paper focus on whether the loan is fully funded instead of successful or not. Variables include borrowers’credibility--credit ratings, lending score, success and failure history, social capital—number of friends, and PPDai’s special verification-Taobao&Dunhuang sellers’loan and non-withdraw safe loan. Then we build Logistic model in order to help borrower predict whether the loan would be fully funded and total prediction rate is89.6%.The model shows that loan amount is positive for a loan to get funded. This is somehow different from the previous research outcome, which might be attributed to the mechanism of PPDai, i.e. the loan amount limit is heavily depending on the borrowers’credit ratings and verifications. This may explain why the larger loan amount is, the easier loan could get fully funded. Additionally, number of friends does not show significant impact on loan as expected. This may attribute to immaturity of the social network mechanism on PPDai. We assume that "number of friends" is just kind of structure social capital which would not quite affect online loan.In the end, refer to previous platform comparisons and quantitative analysis, the paper provides some suggestions on internal control of PPDai, hoping to benefit the business process of PPDai and strengthen the stickiness of users.