Improving the Data Quality for Credit Card Fraud Detection
Rongrong Jing1,2; Hu Tian1,2; Yidi Li2; Xingwei Zhang1,2; Xiaolong Zheng1,2; Zhu Zhang1,3; Daniel Dajun Zeng1,2,3
2022-11
Conference Name2020 IEEE International Conference on Intelligence and Security Informatics (ISI)
Conference Date2022-11
Conference PlaceArlington, VA, USA
Abstract

Label imbalance and data missing are two major challenges in the problem of credit card fraud detection. However, existing matrix completion algorithms are generally difficult and cannot be easily applied to real-world credit card fraud detection since the scale of the normally used dataset is oversized. In this paper, we develop a spectral regularization algorithm to complete the large-scale sparse matrices, and further utilize an over-sampling algorithm to tackle the problem of the imbalance between positive and negative samples. Experimental results on a real-world dataset demonstrate that our model can outperform the state-of-the-art baseline methods. The proposed method could also be extended to other large-scale scenarios where data is missing or labels are imbalanced.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48799
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorXiaolong Zheng
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
3.Shenzhen Artificial Intelligence and Data Science Institude (Longhua)
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Rongrong Jing,Hu Tian,Yidi Li,et al. Improving the Data Quality for Credit Card Fraud Detection[C],2022.
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