Predicting Peer-to-Peer Loan Rates Using Bayesian Non-Linear Regression

Zsolt Bitvai, Trevor Cohn
2015 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Peer-to-peer lending is a new highly liquid market for debt, which is rapidly growing in popularity. Here we consider modelling market rates, developing a non-linear Gaussian Process regression method which incorporates both structured data and unstructured text from the loan application. We show that the peer-to-peer market is predictable, and identify a small set of key factors with high predictive power. Our approach outperforms baseline methods for predicting market rates, and generates substantial profit in a trading simulation.
doi:10.1609/aaai.v29i1.9515 fatcat:z2e3rl5vmncizeehjcfppaenki