A novel credit scoring framework for auto loan using an imbalanced-learning-based reject inference

Abstract

Along with the booming consumer credit market, credit scoring has received an increasing concern in auto financial companies. However, the modeling without rejected applicants and the imbalanced distribution of accepted examples affect the predictive performance. In this paper, we propose a novel framework for credit scoring using an imbalanced-learning-based reject inference. First, we employ an imbalanced learning for the accepted applicant data using Synthetic Minority Over-sampling Technique for reject inference. Second, we conduct reject inference for rejected applicants based on a graph-based semi-supervised learning algorithm, which is called label propagation. Third, we use tree-based ensemble learning models as base classifiers to train the combined training data. Finally, we give an exact experiment for assessment using data from a Chinese auto loan company. The results indicate that the proposed novel framework performs better than comparative models, which represents a progressive method for auto loan.