PDF Ebook Screening in New Credit Markets
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.This paper evaluates the screening ability of lenders in such peer-to- peer markets. Our methodology takes advantage of the fact that lenders do not observe a borrower’s true credit score but only see 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 subjective information 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.
An important function of credit markets is to screen borrowers and allocate credit efficiently based on borrowers’ creditworthiness. Traditionally, banks have played the dominant role in allocating credit partly because they are attributed to have the financial expertise to evaluate borrowers and effectively intermediate capital (Diamond, 1984). While there is a broad consensus on the importance of banks in financial intermediation, the recent banking crisis has highlighted short-comings in the traditional lending models, particularly in allocating credit to smaller borrowers.1 While there is increasing debate in how these short-comings can be addressed, a variety of new lending models offer potentially valuable insights. Peer-to-peer online lending platforms provide a non-hierarchical market-based mechanism that facilitates screening by aggregating information on borrower creditworthiness over multiple (individual) lenders. While such markets may be better at utilizing non-standard/“softer” information, the (peer) lenders typically lack the financial and screening expertise of traditional banks. In this paper, we evaluate whether such lending platforms are able to effectively screen for borrower creditworthiness. Thus, we examine the viability of such lending platforms in improving small borrowers’ credit access, in turn complementing traditional lending models.
Web-based peer-to-peer lending markets, such as Prosper, Zopa, Kiva, Myc4, Lending Club, Pertuity Direct, and Fynanz, have grown dramatically both in number and size. Prosper has funded over $178 million in loans and currently has 830,000 members. These markets are quickly gaining popularity in lending to smaller-scale borrowers such as individuals and small firms, both in developed and developing economies.2 The uncollateralized nature of lending in these online markets makes it particularly attractive for small borrowers who might otherwise turn to payday lenders or credit card debt, often at exorbitant rates (Adams, Einav, and Levin, 2009). However, as non-financial experts dominate peer-to-peer markets, their ability to judge financial risk and information is key to the viability of these markets. While there is some evidence from other contexts, such as prediction markets, that non-experts can extract information effectively (Wolfers and Zitzewitz, 2004), there is scant direct evidence on whether these peer-to-peer markets can effectively screen borrowers and allocate credit.
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