Social collateral, soft information and online peer-to-peer lending: A theoretical model

Zhengchi Liu, Jennifer Shang, Shin yi Wu, Pei yu Chen

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Traditional credit markets have been criticized as inefficient in allocating credits to borrowers. Powered by advanced Internet technology, online Peer-to-Peer (P2P) lending has emerged as an attractive alternative, especially for small borrowers who have limited assets and are in need of funds urgently. Although several empirical studies have examined factors influencing the micro-level lending outcome, there is a lack of understanding on the overall business model of P2P lending, especially its screening mechanism, and how it helps address the deficiency of the traditional credit market. This paper fills this void. First, we develop a theoretical model incorporating two unique features of P2P lending (soft information and social collateral) and show that in P2P, low-risk borrowers could force high-risk ones off the market under very general conditions. As a result, P2P complements traditional credit markets by serving the unserved (low-risk borrowers with little assets) in the traditional credit markets. Second, we further identify the critical operational settings for P2P success, and the impacts of these settings on borrowers’ welfare. Overall, our model and analyses not only contribute to the literature by showing analytically that P2P and the traditional credit markets are complementary, but also provide practical guidance to P2P platform managers regarding their platform design to help reshape business strategies and enhance business opportunities.

Original languageEnglish (US)
Pages (from-to)428-438
Number of pages11
JournalEuropean Journal of Operational Research
Issue number2
StatePublished - Mar 1 2020


  • Finance
  • P2P lending
  • Screening mechanism
  • Social collateral
  • Soft information

ASJC Scopus subject areas

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


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