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Credit risk evaluation has gained substantial attention within financial institutions, serving as a pivotal tool to predict borrower repayment behavior and provide precise credit risk estimations. Traditional credit risk approaches discarded rejected applicants and were built only on accepted applicants, which posed sample selection bias issue. Previous reject inference methods solved the bias issue by incorporating information of rejected applicants. However, these methods assumed that the accepted and rejected samples had identical dimensions. In practical financial scenarios, financial institutions often encounter situations where the dimensions of accepted samples were larger than those of the rejected samples. Therefore, the additional features in accepted samples might not be fully utilized in the previous reject inference. In this study, we proposed a discriminative dual stack sparse auto-encoder (DD-SSAE) reject inference method that was suitable for the real scenarios. The proposed DD-SSAE has the following characteristics: (1) rejected samples were filtered based on our selection mechanism; (2) a stack sparse auto-encoder (SSAE), within a self-taught learning framework, was carried out to incorporate information of the selected rejected samples into the common features of accepted samples; and (3) a data fusion module, consisting of another SSAE network and a data fusion layer, was introduced to combine extra features with common features for accepted samples. The proposed method was verified on a Chinese consumer dataset and the findings illustrated its superiority over four conventional credit scoring models and five previous reject inference models.

Credit risk evaluation has been vital for financial institutions to identify default customers and to avoid financial loss. Machine learning and data mining techniques have been adopted to develop scoring models for enhancing the prediction performance of default customers. However, it is difficult for these machine learning models for explaining the rejection or approval decision-making process to customers and other non-technical personnel. This paper presents an evidence reasoning (ER) rule-based ensemble learning approach for credit risk evaluation considering customer heterogeneity. Firstly, customers are segmented into different groups by $k$-means clustering algorithms and a two-stage weighting method is proposed to determine the significances of attributes by their discriminating powers between groups and within groups. Then, the attribute-related evidence is obtained by Bayesian statistics to represent the relationships between the attributes and credit risks, and a two-stage weighting evidential reasoning (TER) is developed as a base learner for credit scoring. Lastly, multiple base learners TERs are aggregated for evaluating customers’ credit risks. An empirical study on three credit datasets demonstrated that the proposed approach can achieve high performance with good explainability. The predicted results of the model can be well comprehended by providing the contribution of attributes and the activated rules in evidential reasoning processes.