Combination of Random Forests and Neural Networks in Social Lending

Abstract

Social lending, also known as peer-to-peer lending, provides customers with a platform to borrow and lend money online. It is now rapidly gaining its popularity for its superior monetary advantage comparing to banks for both borrowers and lenders. Thus, choosing a reliable is very important, whereas the only method most of the platforms use now is a grading system. In order to better prevent the risks, we propose a method of combining Random Forests and Neural Network for predicting the borrowers’ status. Our data are from Lending Club, a popular social lending platform, and our results indicate that our method outperforms the lending Club good borrower grades.