Abstract
In credit rating, borrowers are classified into some grades, based on their past credit experience, to recognize potential borrowers who are of different probability of default. Classification algorithms are commonly used, which is important to reduce loss for banks or investors. Since multiple classes are imbalanced, as well as losses of misclassification across classes are not uniform, multi-class cost-sensitive classifiers should be paid more attention in credit rating. Based on existing literatures, in this paper, we review several cost-sensitive classifiers and compare them with three assumed cost matrices. The empirical study utilizes data collected from Lending Club which is the largest U.S. P2P loan platform. The results are aimed at giving insights on cost-sensitive classifiers with various performances in credit rating, which are concerned by researchers and practitioners of credit lending.
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