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. Here an experiment will be performed to measure the performance of a model used for classifying peer-to-peer loan data. The aim is to aid the repayment prediction capabilities of peer lenders when analyzing low credit applicants. A binary classification algorithm is used to build the model and applied to actual historical loan data to evaluate performance. Experiment results, visualizations, and key performance indicators are discussed in the work to influence confidence in using the method proposed.