Title: Private Information, Credit Risk and Graph Structure in P2P Lending Networks,
Abstract: This research investigated the potential for improving Peer-to-Peer (P2P)
credit scoring by using "private information" about communications and travels
of borrowers. We found that P2P borrowers' ego networks exhibit scale-free
behavior driven by underlying preferential attachment mechanisms that connect
borrowers in a fashion that can be used to predict loan profitability. The
projection of these private networks onto networks of mobile phone
communication and geographical locations from mobile phone GPS potentially give
loan providers access to private information through graph and location metrics
which we used to predict loan profitability. Graph topology was found to be an
important predictor of loan profitability, explaining over 5.5% of variability.
Networks of borrower location information explain an additional 19% of the
profitability. Machine learning algorithms were applied to the data set
previously analyzed to develop the predictive model and resulted in a 4%
reduction in mean squared error.