A good story can trump a bad credit score in peer-to-peer lending
These days a bad credit score will get you turned away by a bank, but if you tell a good story about that score, you can improve your chances of getting a microloan from a peer-to-peer lender, according to new research from Rice University and the University of Delaware.
The researchers found that in peer-to-peer lending, unverifiable information such as personal narratives and explanations affected lending decisions above and beyond objective, verifiable information such as credit scores and histories.
In two new studies, researchers analyzed data from Prosper.com, America's first peer-to-peer lending marketplace with more than a million members; borrowers and lenders can connect there without going through a bank or institution. Borrowers choose a loan amount, purpose and post a loan listing. Then investors review loan listings and invest in those that meet their criteria. Once the process is complete, borrowers make fixed monthly payments and investors receive a portion of those payments directly to their Prosper account.
In the first study, the researchers Scott Sonenshein and Utpal Dholakia from Rice University's Jones Graduate School of Business and Michal Herzenstein from the University of Delaware -- found that micro-lenders were more likely to offer loans to borrowers who explained, and then admitted or denied, the details of their credit history. For instance, a borrower increased her/his perceived trustworthiness and chance of securing a loan by telling a lender, "I missed several payments on my car loan, which led to an increased interest rate, but I'm paying on time now and have learned from my mistakes" even though there was no evidence to support the claim that the borrower learned from past mistakes. Indeed, 65.3 percent of all loan requests that included such similar statements were funded, compared with only 45.8 percent of the loans that did not include such statements.
"Despite a poor credit grade, the social accounts that borrowers give and identities they create can increase their chances of securing a loan," said Sonenshein, lead author of one of the studies and assistant professor of management. "But relying on those accounts and identities can actually lead lenders to make poorer decisions about which loans to fund."
For example, borrowers who explain their circumstance but deny the details "The credit card company lost my payment and is now working to correct my credit report" have the poorest loan performance. For example, 25 percent of the borrowers who make such claims are late with their payments or have already defaulted, while only 10.5 percent of borrowers who did not make such claims are late or have defaulted.
In the second study, Sonenshein and his co-authors analyzed the six different identities trustworthy, successful, economic hardship, hardworking, moral and religious that borrowers constructed for themselves in the loan application's optional essay. They found that borrowers in their sample could lower their costs by almost 30 percent and saved about $375 in interest charges by using a "trustworthy" identity.
Borrowers who claimed the "trustworthy" or "successful" identities also received higher loan funding than those who did not 121 percent versus 82 percent. Those who described themselves as "religious" were less likely to get a loan.
According to the study, borrowers with lower credit grades constructed more identities to compensate for their objectively poor circumstances. The more identities the borrowers constructed, the more likely lenders were to fund the loan and reduce the interest rate but the less likely the borrowers were to repay the loan: 29 percent of borrowers with four identities defaulted, whereas 24 percent with two identities and 12 percent with no identities defaulted.
Claiming one additional identity decreased the final interest rate by 12.75 percent for the loans in their sample. So by claiming one identity, compared with zero, the borrower paid $177.98 less after three years on a loan of $8,305 -- the average loan requested within the dataset.
"By analyzing the reasons borrowers give and the identities they construct, we can predict payback status over and beyond more objective factors such as credit scores," Sonenshein said. "In a sense, it offers a way of assessing borrowers in ways that harp back to the earlier days of community banking when lenders knew their customers."
Further analysis showed that a "trustworthy" identity predicts that a loan will be paid back early, a "moral" identity predicts payment on time and an "economic hardship" identity predicts a default or late payment.
"Despite being authentic about their difficulties, the borrowers are using an 'economic hardship' identity to gain empathy, but it's a tactic lenders should view as a warning sign," Sonenshein said. "Ultimately, these borrowers lack either the ability or willingness to fulfill loan obligations."