P2P Lending News

A team of researchers headed by Jefferson Duarte from Rice University’s Jones School of Management has published a study entitled “Trust and Credit“. In their research,  the team took photos posted on Prosper loan listings, and had 25 users from Amazon’s Mechanical Turk site evaluate each picture for “trustworthiness”.

Each of the 25 users was presented a picture of a Prosper borrower, and asked to rate their trustworthiness on a scale from 1-5. They were also asked to predict the probability that that borrower would repay a loan of $100.

The researchers found that the R², or coefficient of determination, between perceived borrower trustworthiness (as determined by humans looking at their pictures) and the borrower’s credit grade (as determined by the Experian credit reporting agency) was between 1% and 1.7%, which seems incredibly low. This means that your first impression of a borrower’s trustworthiness may be just as valuable as your peek at their credit score. Since Prosper doesn’t validate borrower photos, it’s unclear how much value this information has on such a platform. But in a real-world situation (such as a bank lobby, or if Prosper were to build brick & mortar locations), there may be an opportunity to put this information to use.

How did this play out in actual listings funded and loans made? Not surprisingly, borrowers who looked un-trustworthy were less likely to be funded. Even among those who did get funded by Prosper lenders, un-trustworthy borrowers paid on average 1.82% more interest per annum for their loan.

The researchers also made a couple of interesting points about Prosper, citing it as a unique example for their research. First, photographs to accompany loan information are unique. Second, behavioral psychology rarely gets to measure the input of thousands of unbiased actions; in the case of Prosper, thousands of lenders express their desires through their bids, and because Prosper provides all of their data through a downloadable file and web services API, researchers and academics have the ability to do any analysis they want. Third,  economics rarely has the ability to see failed outcomes – typically, only successful matches can be measured; on Prosper, even failed listings are saved and provided through the API.

Here is the full text of the paper: (Source)