Abstract
Credit scoring is the risk assessment of customers. A reliable credit scoring model can provide decision support for financial institutions. In this paper, the logistic regression model with elastic net penalty (LR-EN) is proposed to assess personal credit score. Results on German bank personal credit data show that the proposed method can greatly improve classification precision of “bad” customer compared with other three methods. In addition, the attributes selected by LR-EN are well interpreted.
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Acknowledgments
This work was supported by the Natural Science Foundation of China (61203293, 61702164, 31700858), Scientific and Technological Project of Henan Province (172102210047, 162102310461, 172102310535), Natural Science Foundation of Henan Province (162300410184), Foundation of Henan Educational Committee (18A520015), Scientific Research Project of Zhengzhou (153PKJGG128), Foundation for University Young Key Teacher of Henan Province (2016GGJS-079).
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Li, J., Chang, M., Tian, P., Chen, L., Mu, X. (2020). Personal Credit Scoring via Logistic Regression with Elastic Net Penalty. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 592. Springer, Singapore. https://doi.org/10.1007/978-981-32-9682-4_44
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DOI: https://doi.org/10.1007/978-981-32-9682-4_44
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