Putting the brain to work: Credit index evaluation for P2P lending based on artificial neural network modeling

Abstract

Effective assessment of a borrower's various credit indexes is key for unravelling the problem of information asymmetry in the context of Peer-to-Peer Lending (P2P). Mitigating adverse selection of high default potential borrowers continues to plague P2P lending platforms. In order to understand which factors determine borrower credit status (ie. loan approval, loan repayment potential, risk of default), this study renders an Artificial Neural Network Model on one of the most popular P2P lending platforms. Our results show that the interest rate, the ratio of loan to income and the loan term are the most important indicators in reflecting the borrower’s credit status, while the frequency of inquiries, the borrowing category have a relatively low degree of importance. This study finds that the borrower’s credit index status is better explained at the lower quantiles and becomes more difficult to discern at higher quantiles. This work also finds that for longer loan terms, the borrower repayment pressure and the default rates rise with higher loan-to-income ratios and higher interest rates. Additionally, we find that higher credit rankings and higher expected returns lead to higher probabilities of defaulting. To reduce the probability of borrower default, this study recommends building lending groups or lending pools, selecting higher income credit candidates and increasing credit limits. To validate our results, we perform robustness tests that modify the learning coefficient and the training-to-validation data ratio in order to show that the empirical results of this paper are robust and effective.