Screening in New Credit Markets:Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending?

By Rajkamal Iyer, Asim Ijaz Khwaja, Erzo F. P. Luttmer, Kelly Shue. 2009

186 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/59e85150cda5cb473018843414612260a1e620ac

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Citations in the corpus

Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending (2013, unknown): “The flavor of our findings is similar to that of Iyer et al. (2009), who find that loans in the P2P marketplace seem to reflect default information beyond the traditional hard credit variables.”
Herding behavior in online P2P lending: An empirical investigation (2012, background): “Kauffman and Li (2003) found that corporate decision makers in IT adoption invest in what was chosen by earlier adopters.”
Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China (2017, background): “The study of Iyer et al. (2009) has illustrated that borrowers can use both hard and soft data about the borrowers to assess onethird of the borrower’s credit risk (Iyer et al. 2009).”
Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending (2015, background): “While there are few studies which explored credit screening problem in the P2P lending platforms, this research differs from the prior research in various aspects (see, for example, Iyer et al., 2009 and Lin et al., 2013).”
Trust and Credit: The Role of Appearance in Peer-to-peer Lending (2012, background): “For example, closely related to our study, Iyer et al. (2009) point out that Prosper lenders see only coarse borrowers’ credit grades and infer about one-third of the information in the actual borrowers’ credit score from “soft” information in borrowers’ applications.”
Rational Herding in Microloan Markets (2012, background): “…determinants of funding outcomes on Prosper, such as physical appearance (Ravina 2008), lenders learning from hard versus soft information (Iyer et al 2009), perceived trustworthiness (Duarte et al. 2010), borrowers' identity claims (Herzenstein et al. 2011b), taste-based discrimination…”