A semi-supervised reject inference framework with hierarchical heterogeneous networks for credit scoring

Credit scoring is a popular tool for loan assessment, i.e., deciding whether to accept or reject a loan application. Traditional research into learning for credit scoring has only applied historically accepted samples without rejected applicants whose true repayment performance is absent, thereby causing both sample selection bias and wasting data. Some methods have been proposed for inferring rejected samples but they are still affected by several open problems, especially for medium- and long-term loan applications with a higher rejection rate. In particular, the heterogeneous relationships between accepted and rejected applications have not been well studied. Moreover, the complex repayment behaviors resulting from long repayment terms may lead to poor learning performance. Thus, we propose a reject inference framework with Semi-supervised Hierarchical Heterogeneous Network (S2HN) for credit scoring. We introduce a hierarchical heterogeneous network for revealing the complex connections between accepted and rejected applications, and use prospective heterogeneous repayment patterns as auxiliary information through clustering and a two-layer prediction architecture. Extensive experiments conducted based on real-world data sets demonstrated the effectiveness of our proposed method.