Journal
DECISION SUPPORT SYSTEMS
Volume 137, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.dss.2020.113366
Keywords
Reject inference; Unsupervised transfer learning; Three-way decision theory; Credit scoring
Categories
Funding
- Humanities and Social Sciences Foundation of the Ministry of Education of China [17YJC630119]
- National Natural Science Foundation of China [U1811462, 71910107002, 71725001]
- Chinese National Funding of Social Sciences [19ZDA092]
- Applied Basic Research Program of Sichuan Province [2020YJ0042]
- Fundamental Research Funds for the Central Universities [JBK2003020]
- project of Research Center for System Sciences and Enterprise Development [Xq20B08]
- project of Fintech Innovation Center of Southwestern University of Finance and Economics
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There has been significant research into reject inference, with several statistical methods and machine learning techniques having been employed to infer the possible repayment behavior of rejected credit applicants. This study proposes a novel three-stage reject inference learning framework using unsupervised transfer learning and three-way decision theory that integrates: (1) the rejected credit sample selection using three-way decision theory, (2) higher-level representations to transfer learning from both accepted and selected rejected credit samples; and (3) credit scoring using the reconstructed accepted credit samples. This method was found to both perform well for reject inference and handle negative transfer learning problems. The numerical results were validated on Chinese credit data, the results from which demonstrated the superiority of the proposed reject inference method for credit risk management applications.
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