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

doi:10.1109/TCSS.2023.3276274

	Transductive Semi-Supervised Metric Network for Reject Inference in Credit Scoring
	Guo, Zhiyu 1,2; Ao, Xiang 1,3,4; He, Qing 1,2
	2023-05-25
发表期刊	IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
ISSN	2329-924X
页码	10
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.
关键词	Credit scoring metric learning reject inference transductive learning
DOI	10.1109/TCSS.2023.3276274
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Cybernetics ; Computer Science, Information Systems
WOS记录号	WOS:001007583100001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21212
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.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Inst Intelligent Comp Technol, Suzhou 215124, 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.