Reject Inference Using Discriminative Dual Stack Sparse Auto-Encoders for Consumer Credit Risk Evaluation

Gang Kou, Siqi Weng (), Feng Shen and Fahd Saleh S. Alotaibi ()
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Gang Kou: ,‡School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, P. R. China†School of Digital Media Engineering and Humanities, Hunan University of Technology and Business, Changsha 410205, P. R. China
Siqi Weng: ,‡School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, P. R. China
Feng Shen: School of Finance, Southwestern University of Finance and Economics, Chengdu 611130, P. R. China4Engineering Research Center of Intelligent Finance, (Ministry of Education), Southwestern University of Finance and Economics, Chengdu 611130, P. R. China
Fahd Saleh S. Alotaibi: Information Systems Department, Faculty of Computing and Information, King Abdulaziz University, P.O. Box 80213, Jeddah 21589, Saudi Arabia

International Journal of Information Technology & Decision Making (IJITDM), 2025, vol. 24, issue 01, 327-353

Abstract: 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.

Keywords: Reject inference; isolation forest; transfer learning; stack sparse auto-encoder; data fusion; credit risk evaluation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219622025500038

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