A social recommendation mechanism for peer-to-peer lending

Ting Kai Hwang, Yung-Ming Li, Jun Fei Wan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Compared with traditional bank, Peer-to-Peer (P2P) lending is claimed to benefit both borrowers and lenders. However, because of the information asymmetry online, much fewer investors dare to use this new alternative finance comparing to the potential market. Moreover, for borrowers, they always spend much more time to wait for the bidders during the initial phase than the other phases. This research has proposed a social recommendation mechanism to help borrowers to find suitable lenders based on the theory of social capital and techniques of social computing and to attract more potential lenders to join in the P2P lending utilizing the theory of social influence. An experiment simulating the process of P2P lending has been executed in this paper. With comparisons in multiple dimensions, it shows our proposed mechanism can effectively improve the bidding rate for borrowers and the lenders are willing to lend out more money in each bid when they have social relationships with borrowers.

Original languageEnglish
Title of host publicationAmericas Conference on Information Systems 2018
Subtitle of host publicationDigital Disruption, AMCIS 2018
PublisherAssociation for Information Systems
ISBN (Print)9780996683166
StatePublished - 2018
Event24th Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018 - New Orleans, United States
Duration: 16 Aug 201818 Aug 2018

Publication series

NameAmericas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018

Conference

Conference24th Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018
Country/TerritoryUnited States
CityNew Orleans
Period16/08/1818/08/18

Keywords

  • Information asymmetry
  • Peer-to-Peer lending
  • Social influence
  • Social recommendation

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