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Who can get money? Evidence from the Chinese peer-to-peer lending platform

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Abstract

This paper explores how borrowers’ financial and personal information, loan characteristics and lending models affect peer-to-peer (P2P) loan funding outcomes. Using a large sample of listings from one of the largest Chinese online P2P lending platforms, we 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. We also find that the unique offline process in the Chinese P2P online lending platform exerts significant influence on the lending decision. We discuss the implications of our results for the design of big data-based lending markets.

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Notes

  1. In the UK, the peer-to-peer lending industry lent more than 2.2 billion GBP in 2015 and this number is more than twice that of the previous year, according to the figures released by the Peer-to-Peer Finance Association (P2PFA). In the US, loans originated by P2P lenders reached 20 billion USD by 2015, again double the amount of the previous year. In the Chinese P2P market in 2015, lending volume was about 982.304 billion CNY, a rise of 388% on the previous year’s figures. The number of P2P platforms now stands at 2595 as reported by WDZJ.COM.

  2. The People’s Bank of China (the central bank) has started the collection of credit history of individuals and entities and calculating of credit scores, but the credit information is not shared with non-banking institutions, such as P2P lending platforms.

  3. http://www.ecns.cn/business/2016/01-02/194408.shtml

  4. People’s Bank of China and 10 other Ministries and Commissions jointly issued “Guiding Opinions on Promoting the Healthy Development of Internet Finance” in 2015.

  5. See ft.com. Link: http://www.ft.com/cms/s/0/4ca011f4-c88f-11e5-a8ef-ea66e967dd44.html#axzz4Di9nuZIy

  6. The smart-money is an intelligent lending feature that allows investors to automatically allocate their money across the listings by selecting their desired risk appetite and saving plan objectives. However, Renrendai stopped the smart-money lending mode at the end of 2012.

  7. The Shanghai Branch of China Merchants’ Bank has been providing a custodian service for the risk reserve fund. of Renrendai.com and issues monthly report on its fund flows.

  8. We divide China into six economic regions plus an extra category made up of four municipalities. The six regions are the centre and south (Henan, Hubei, Hunan, An’hui and Jiangxi), the east coast (Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan), the north (Inner Mongolia, Hebei and Shanxi), the northeast (Jilin, Liaoning and Heilongjiang), the southwest (Sichuan, Guizhou, Yunnan, Guangxi and Tibet), and the northwest (Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang). The four municipalities are Beijing, Shanghai, Tianjin, and Chongqing.

  9. In the sample, we find there are no defaults on the loans which were originated by the offline authentication and third-party referral lending models. All default loans come from the loans which were funded based on the pure online lending process. Therefore, we examine the determinants of default probability based on the sample which only includes the loans made by the pure online lending process.

  10. We also use Logit models to estimate the probability that a listing is funded successfully and a loan default is used as a robustness check. The Logit regression results are consistent with those from the Probit models.

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Acknowledgements

We thank the Editors, Ram Ramesh and Raghav Rao, the guest editors, Douglas Cumming, Sofia Johan, and Denis Schweizer for their helpful and valuable suggestions. We are grateful for useful comments from the participants of the 2nd microfinance and rural finance conference in Aberystwyth. Qizhi Tao acknowledges support from the Fundamental Research Funds for the Central Universities (Grant No. JBK160921).

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Authors and Affiliations

  1. School of Finance, Southwestern University of Finance and Economics, Chengdu, 611130, China

    Qizhi Tao & Ziming Lin

  2. Business School, University of Aberdeen, Dunbar Street, Aberdeen, AB24 3QY, UK

    Yizhe Dong

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  1. Qizhi Tao
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Correspondence to Yizhe Dong.

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Table 7 Variables and Definitions
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Tao, Q., Dong, Y. & Lin, Z. Who can get money? Evidence from the Chinese peer-to-peer lending platform. Inf Syst Front 19, 425–441 (2017). https://doi.org/10.1007/s10796-017-9751-5

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