This paper – prepared by Qizhi Tao, Southwestern University of Finance and Economics (SWUFE), Yizhe Dong, University of Aberdeen – Business School, and Ziming LIN, Southwestern University of Finance and Economics, published on Information Systems Frontiers – explores how borrowers’ financial and personal information, loan characteristics and lending models affect peer-to-peer (P2P) loan funding outcomes. The researchers, using a large sample of listings from one of the largest Chinese online P2P lending platforms, find that those borrowers earning a higher income or who own a car are more likely to receive a loan, pay lower interest rates, and are less likely to default. The credit grade assigned by the lending platform may not represent the creditworthiness of potential borrowers. The Researchers also find that the unique offline process in the Chinese P2P online lending platform exerts significant influence on the lending decision. Finally, the researchers discuss the implications of their results for the design of big data-based lending markets.
Online peer-to-peer (P2P) lending has recently emerged as a new form of loan origination for the credit market. It is defined as peer-to-peer unsecured lending between lenders and borrowers through online platforms without the involvement of financial institutions. This type of lending market place is designed to supplement traditional bank lending in order to meet the small-loan needs of individuals and small-to-medium enterprises (SMEs), which often encounter difficulty in borrowing money from traditional lending institutions.
Since the first online P2P lending platform, Zopa, was established in the UK in 2005, numerous P2P lending platforms have emerged all over the world, such as the Prosper and Lending Club in the US, is ePankur and Auxmoney in Europe, SocietyOne in Australia, and Renrendai and CreditEase in China. Concurrently this market has grownexponentially.
Please, read the full paper at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2863360
