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Transductive Semi-Supervised Metric Network for Reject Inference in Credit Scoring

文献类型：期刊论文

作者Guo, Zhiyu2,3; Ao, Xiang1,2,4; He, Qing2,3
刊名IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
出版日期2023-05-25
页码10
ISSN号2329-924X
关键词Credit scoring metric learning reject inference transductive learning
DOI10.1109/TCSS.2023.3276274
英文摘要Credit scoring is an essential technique for credit risk management in the financial industry. However, most credit scoring models face the challenge of reject inference, which refers to the lack of post-loan performance data for rejected applicants, leading to sample selection bias and inaccurate credit assessment. Traditional credit scoring methods tackle this issue by assuming that the missing labels for rejected samples are missing at random (MAR) and by measuring sample similarity directly in the original feature space. Nevertheless, these strategies are not suitable for real-world business scenarios. Inspired by metric learning and transductive learning, we propose a novel credit scoring model called transductive semi-supervised metric network (TSSMN), which formalizes reject inference as a semi-supervised binary classification problem with the prior assumption of missing not at random (MNAR). TSSMN consists of two interconnected modules: the embedding metric network (EMN) that maps samples from the original feature space to the metric space for similarity measurement, and the transductive propagation network (TPN) that performs label propagation based on sample similarity. We evaluate TSSMN on a real-world credit dataset and compare it with traditional credit scoring methods. The results indicate that TSSMN can overcome sample selection bias and more accurately classify credit applicants. Therefore, TSSMN has the potential to enhance credit risk assessment in real-world business scenarios.
资助项目National Key Research and Development Plan[2022YFC3303302] ; National Natural Science Foundation of China[61976204] ; Alibaba Group through Alibaba Innovative Research Program ; Project of Youth Innovation Promotion Association Chinese Academy of Science (CAS), Beijing Nova Program[Z201100006820062]
WOS研究方向Computer Science
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001007583100001
源URL[http://119.78.100.204/handle/2XEOYT63/21211]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Ao, Xiang
作者单位1.Inst Intelligent Comp Technol, Suzhou 215124, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Guo, Zhiyu,Ao, Xiang,He, Qing. Transductive Semi-Supervised Metric Network for Reject Inference in Credit Scoring[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2023:10.
APA Guo, Zhiyu,Ao, Xiang,&He, Qing.(2023).Transductive Semi-Supervised Metric Network for Reject Inference in Credit Scoring.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,10.
MLA Guo, Zhiyu,et al."Transductive Semi-Supervised Metric Network for Reject Inference in Credit Scoring".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2023):10.

入库方式： OAI收割

来源：计算技术研究所

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