Affiliations: [a] School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
| [b] Guanghua School of Management, Peking University, Beijing, China
| [c] School of Management, Air University Islamabad, Pakistan
Correspondence:
[*]
Corresponding author: Umair Akram, Guanghua School of Management, Peking University, Beijing. Tel.: +86
13120333936; E-mail: akram.umair88@pku.edu.cn.
Abstract: The development of online peer-to-peer lending is in full bloom due to the support of network technology and information technology. More and more people are keen to participate in them. Online P2P lending platforms provide services where investors lend money to borrowers without the involvement of traditional financial institutions. Due to its convenience, the platforms have attracted many investors and borrowers. However, these investors may suffer a significant loss if they cannot make better borrowing decisions based on prediction credit risk. The purpose of this article is to study the relationship between successful borrowing times (SBT) and successful loans and default loans. This article further explores SBT plays an importance of the role in online P2P lending. Data was collected from PaiPaiDai, an online P2P lending platform in China. Logistic modeling was employed to test the proposed hypotheses. The results show that the probability of default loan by borrowers with more SBT is low, and the probability of successful loan is higher. It shows that investors are more willing to choose borrowers with more SBT. The investors can effectively recognize the value of SBT in the transaction process, and can identify credit risk through SBT in the online P2P lending market. SBT can predict credit risk, which suggests the proposed method has favorable potential being implemented in real-world P2P lending platforms.
