Location

Hilton Waikoloa Village, Hawaii

Event Website

http://www.hicss.hawaii.edu

Start Date

1-4-2017

End Date

1-7-2017

Description

In peer-to-peer (P2P) lending, soft information, such as borrowers’ facial features, textual descriptions of loan applications and so on, are regarded as potential signals to screen borrowers. In this study, we examine the signaling effect of a new category of soft information- social media information. Leveraging a unique dataset that combines loan data from a large P2P lending company with social media presence data from a popular social media site, and two natural experiments, we find two forms of social media information that act as signals of borrowers’ creditworthiness. First, borrowers’ choice to self-disclose their social media account is a predictor of their default probability. Second, borrowers’ social media presence, such as their social network and social media engagement, are also predictors of default probability. This study proffers new insights for the screening process in P2P lending and novel usage of social media information.

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Jan 4th, 12:00 AM Jan 7th, 12:00 AM

Borrower’s Self-Disclosure of Social Media Information in P2P Lending

Hilton Waikoloa Village, Hawaii

In peer-to-peer (P2P) lending, soft information, such as borrowers’ facial features, textual descriptions of loan applications and so on, are regarded as potential signals to screen borrowers. In this study, we examine the signaling effect of a new category of soft information- social media information. Leveraging a unique dataset that combines loan data from a large P2P lending company with social media presence data from a popular social media site, and two natural experiments, we find two forms of social media information that act as signals of borrowers’ creditworthiness. First, borrowers’ choice to self-disclose their social media account is a predictor of their default probability. Second, borrowers’ social media presence, such as their social network and social media engagement, are also predictors of default probability. This study proffers new insights for the screening process in P2P lending and novel usage of social media information.

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