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.
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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.
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Lei, K., Xie, Y., Zhong, S. et al. Generative adversarial fusion network for class imbalance credit scoring. Neural Comput & Applic 32, 8451–8462 (2020). https://doi.org/10.1007/s00521-019-04335-1
- Credit scoring
- Class imbalance
- Generative adversarial network
- Feature fusion