CF-NN: a novel decision support model for borrower identification on the peer-to-peer lending platform

Abstract

With the prevalence of online individual micro-loans, an increasing number of peer-to-peer lending platforms have been established during the last ten years. One main problem for these platforms is to accurately identify the ‘bad’ applicants with high default risk. In this paper, we propose a CF-NN model that combines neural network and collaborative filtering for identifying high-risk borrowers. It is demonstrated in the experimental analysis that the CF-NN model significantly outperforms other widely used data mining models on the identification of bad borrowers. Moreover, the experimental results show that, to achieve the best performance in borrower identification, the CF-NN model should be equipped with parameters of intermediate values.