Generative adversarial fusion network for class imbalance credit scoring

  • Kai Lei
  • Yuexiang Xie
  • Shangru Zhong
  • Jingchao Dai
  • Min Yang
  • Ying ShenEmail author
Original Article


Credit scoring on class imbalance data, where the class of defaulters is insufficiently represented compared with the class of non-defaulters, is an important but challenging task. In this paper, we propose an imbalanced generative adversarial fusion network (IGAFN) to cope with the class imbalance credit scoring based on multi-source heterogeneous credit data. Concretely, we design a fusion module to integrate the heterogeneous credit data from multiple sources into a unified latent feature space. A generative adversarial network-based balance module is then designed to generate latent representations of new samples for the minority class of the imbalanced datasets. The performance of IGAFN is compared against multiple conventional machine learning and deep learning algorithms. Extensive experiments show that the proposed IGAFN exhibits significantly better performance than the compared methods on two real-life datasets.


Credit scoring Class imbalance Generative adversarial network Feature fusion 



This work was financially supported by the Shenzhen Project (ZDSYS201802051831427), National Natural Science Foundation of China (No. 61602013), and the Shenzhen Fundamental Research Project (No. JCYJ20170818091546869). Min Yang was sponsored by CCF-Tencent Open Research Fund.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Shenzhen Key Lab for Information Centric Networking & Blockchain Technology (ICNLAB), School of Electronics and Computer Engineering (SECE)Peking UniversityShenzhenPeople’s Republic of China
  2. 2.PCL Research Center of Networks and CommunicationsPeng Cheng LaboratoryShenzhenPeople’s Republic of China
  3. 3.Shenzhen Institutes of Advanced Technology (SIAT)University of the Chinese Academy of SciencesBeijingPeople’s Republic of China

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