Loan default prediction using a credit rating-specific and multi-objective ensemble learning scheme

Abstract

For the consumer lending industry, credit risk assessment is a crucial task for evaluating the default probability of loan applications given the potential loss caused by default. In particular, loan default prediction is a challenging classification problem due to the highly imbalanced class distribution and a model with both strong default identification ability and overall classification accuracy is exceedingly hard to achieve. Furthermore, most existing studies analyze loans with distinct credit ratings as a whole, ignoring the important relationship between class imbalance and specific credit rating categories. Meanwhile, considering loan companies' different risk preferences when reviewing loan applications, this paper proposes a rating-specific multi-objective ensemble learning framework. Specifically, a credit rating-specific modeling strategy is used to construct candidate models for subpopulations of loans having similar default risk. A multi-objective ensemble learning method is developed using an effective imbalanced classification method, One-Class SVM, as the base learning algorithm. A multi-objective evolutionary process is employed for ensemble construction and an adaptive classification boundary adjustment process is proposed to improve the imbalanced classification performance. To validate the proposed methodology, we conduct comprehensive experimental studies and compare the proposed method with several benchmark algorithms on an industry provided data set. Our experimental results demonstrate the advantage of a rating-specific strategy and also demonstrate the superiority of the proposed multi-objective ensemble learning method. Important managerial implications in the consumer lending industry are discussed.