- Open Access
A process model on P2P lending
© Wang et al.;licensee Springer. 2015
- Received: 12 May 2015
- Accepted: 12 May 2015
- Published: 9 June 2015
Online peer-to-peer lending (P2P lending) is booming as the popularity of e-finance. To develop a conceptual model for the P2P lending process is great valuable for managers to tack the issues of marketing, management and operation.
In this paper, we focus on the P2P lending process model and provide a comparative analysis comparing with traditional bank loan process.
Firstly, our model shows that the information flow in P2P lending is more frequent and transparent. Secondly, the model reveals that P2P lending uses a quite different credit audition method, which relies on information and the decision model in the P2P systems. Thirdly, the loan management is not complete normally in P2P lending, because most P2P companies do not have the post-loan records of borrowers.
These findings inspire future studies and practices on P2P lending process and key technologies.
- P2P landing
- Process model
Online peer-to-peer lending (P2P lending) is booming as the popularity of e-finance (Kiisel 2013; Berger and Gleisner 2009). This innovative financial activity refers to unsecured direct loans between lenders and borrowers through online platforms without the intermediation of any financial institutions (Lin et al. 2013; Greiner and Wang 2010; Sorbe 2009). Since the first P2P lending website, Zopa, established in U.K., P2P lending has spread all over the world, such as Prosper in U.S., Smava in Germany, Popfunding in Korea, and Ppdai in China. Prosper (https://www.prosper.com/) is one of the largest lending platforms in the world and had attracted 1.96 million registered members and had facilitated over $635 million in loans by August 2013. Ppdai (http://www.ppdai.com/) is one of the largest lending platforms in China. By the end of August 2013, it had attracted over 500,000 registered members. To the end of 2014, there are 1575 P2P lending companies in China, and the total trading value is up to 25.28 billion Yuan (about 4 billion US$).
Compared with traditional bank loans, P2P lending has its own features. Firstly, lenders make direct investments on the lending website, and they can learn the detailed information about online borrowers. So the information asymmetry is low in P2P lending. Secondly, the lending website provides a variety of functions that enable borrowers to indicate their creditability. It also provide functions for lenders to search loan request, do comparisons, and finally make a decision. So the open web platform actually observe the activities on both sides, say, the borrower side and the lender side. Collectively, it is presents a good opportunity to study the lending process. Thirdly, P2P borrower’ credit is rated online. It relies on a large amount of web information and probably resort to data mining techniques. So the basic operation method in P2P lending is different from that in traditional bank loan.
Therefore, to develop a conceptual model for the P2P lending process is great valuable for managers tackling the issues on marketing, management and operation. In this paper, we will focus on the P2P lending process model and provide a comparative analysis compared with traditional bank loan process on both aspects of money flow and information flow.
The rest of the paper is organized as follows: Background study reviews relevant prior work on P2P lending. P2P lending process describes the P2P lending process. Finally, we discuss the findings and conclude the paper in Conclusions.
P2P lending model has attract great attentions from both industrial and academic fields. In the financial industry, P2P model provides a new pattern on group or crowd financial product design and management. For example, (Perlman 2012) propose an innovative group financial management system in his pattern (Chen and Han 2012) do a comparative study on P2P lending products between the USA and China. In the academic field, user behavior pattern and credit or trust model are inspect in the P2P lending scenario (Zhang et al. 2014; Klafft 2008; Herrero-Lopez 2009). For example, (Lee and Lee 2012) study the herding behavior in the P2P lending market where seemingly conflicting conditions and features of herding are present. They find strong evidence of herding and its diminishing marginal effect as bidding advances (Lin et al. 2013) find the online friendships of borrowers act as signals of credit quality. Friendships increase the probability of successful funding, lower interest rates on funded loans, and are associated with lower ex post default rates (Duarte et al. 2012) investigate the role appearance plays in financial transactions. They find that borrowers who appear more trustworthy have higher probabilities of having their loans funded. Moreover, borrowers who appear more trustworthy indeed have better credit scores and default less often. This research is quite similar to (Yang 2014), who use photographs in online P2P lending websites to study the transactional behaviors.
Process Model is a standard for business process modeling that provides a graphical notation for specifying business processes in a Business Process Diagram (BPD). The objective is to support business process management, for both technical users and business users, by providing a notation that is intuitive to business users (Wang et al. 2009) propose a novel methodology called Policy-Driven Process Mapping (PDPM) for extracting process models from business policy documents, it is the first systematic approach to the discovery of process models from business policies.
Therefore, different from previous studies which cover some particular aspects in P2P lending, we want to study P2P lending process model and how the data is flow in them. It would be great valuable to improve operations on a managerial level.
P2P lending provides users more privilege in choosing the lending manner and lending objects. So the information flow in P2P lending is more frequent and transparent.
P2P lending uses a quite different credit audition method. It relies on information available in the system and the decision model. So IT techniques, e.g. big data analysis, data mining, on credit audition are key points in P2P lending.
The loan management is not very good in P2P lending, because it doesn’t track the post-loan information on borrowers.
It is notable that we get these conclusions just from comparing the P2P lending process and traditional bank loan process. Further studies based on the process model include three directions. First, we want to formalize the P2P process model. Model formalization is valuable for process simulation and validation. Second, big data analysis techniques and models are needed to predict risk in credit audition. Third, the post-loan management is a critical problem in P2P lending process design.
The work described in this paper was partially supported by a grant from the Shenzhen Municipal Science and Technology R&D Funding—Basic Research Program (Project No. JCYJ20140417105742712).
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