Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending

Abstract

Online peer-to-peer (P2P) lending is a new form of loans. Different from traditional banks, lenders provide loans to borrowers directly through P2P platforms. Since many P2P loans are unsecured personal loans, credit rating of loans is vital to control default risk and improve profit for lenders and platforms. Standard binary classifiers are inappropriate in P2P lending because there are multiple credit classes and misclassification costs vary largely across classes in P2P lending. Though there are a few works that studied cost-sensitive classifiers in P2P lending, none of them have analyzed this issue from the perspective of multi-class classifications and measured misclassification costs of different credit grades using real losses and opportunity costs. The objective of this paper is to model credit rating in P2P lending as a cost-sensitive multi-class classification problem. We proposed a misclassification cost matrix for P2P credit grading with a set of equations and models to calculate the costs. An experiment using publicly available data from Lending Club was conducted to validate the usefulness of the proposed misclassification cost matrix. The results showed that the cost-sensitive classifiers can significantly reduce the total cost, which is essential for the survival and profitability of P2P platforms.