Two class Bayes point machines in repayment prediction of low credit borrowers.

Decision-making in the peer-to-peer loan market has not been studied as extensively as traditional lending mostly because of the perceived risk in dealing with low credit borrowers seeking funding alternatives. We develop a machine learning-based approach to test the viability and usefulness in peer-to-peer loan repayment predictions among low credit borrowers. This analysis provides potential benefits that could strengthen the lending market with a more reliable method of identifying applications from promising candidates with low credit.