Screening in new credit markets: can individual lenders infer borrower creditworthiness in peer-to-peer lending?

The current banking crisis highlights the challenges faced in the traditional lending model, particularly in terms of screening smaller borrowers. The recent growth in online peer-to-peer lending marketplaces offers opportunities to examine different lending models that rely on screening by multiple peers. While these market-based, non-hierarchical structures potentially offer screening advantages, especially in utilizing soft information, individual lenders likely lack financial expertise and lending experience. This paper evaluates whether lenders in such peer-to-peer markets are able to use borrower information to infer creditworthiness. We examine this ability in one such online market using a methodology that takes advantage of lenders not observing a borrower’s true credit score but only seeing an aggregate credit category. We find that lenders are able to use available information to infer a third of the variation in creditworthiness that is captured by a borrower’s credit score. This inference is economically significant and allows lenders to lend at a 140-basis-points lower rate for borrowers with (unobserved to lenders) better credit scores within a credit category. While lenders infer the most from standard banking "hard" information, they also use non-standard (subjective) information. Our methodology shows, without needing to code information contained in the pictures or personal descriptions posted by borrowers, that lenders learn even from such "softer" information, particularly when it is likely to provide credible signals regarding borrower creditworthiness. Our findings highlight the screening ability of peer-to-peer markets and suggest that these emerging markets may provide a viable complement to traditional lending markets, especially for smaller borrowers.