A credit risk model with small sample data based on G-XGBoost

Li, Jian, Liu, Haibin, Yang, Zhijun ORCID logoORCID: https://orcid.org/0000-0003-2615-4297 and Han, Lei (2021) A credit risk model with small sample data based on G-XGBoost. Applied Artificial Intelligence: An International Journal, 35 (15) . pp. 1550-1566. ISSN 0883-9514 [Article] (doi:10.1080/08839514.2021.1987707)

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Currently existing credit risk models, e.g., Scoring Card and Extreme Gradient Boosting (XGBoost), usually have requirements for the capacity of modeling samples. The small sample size may result in the adverse outcomes for the trained models which may neither achieve the expected accuracy nor distinguish risks well. On the other hand, data acquisition can be difficult and restricted due to data protection regulations. In view of the above dilemma, this paper applies Generative Adversarial Nets (GAN) to the construction of small and micro enterprises (SMEs) credit risk model, and proposes a novel training method, namely G-XGBoost, based on the XGBoost model. A few batches of real data are selected to train GAN. When the generative network reaches Nash equilibrium, the network is used to generate pseudo data with the same distribution. The pseudo data is then combined with real data to form an amplified sample set. The amplified sample set is used to train XGBoost for credit risk prediction. The feasibility and advantages of the G-XGBoost model are demonstrated by comparing with the XGBoost model.

Item Type: Article
Research Areas: A. > School of Science and Technology > Natural Sciences
Item ID: 34630
Notes on copyright: © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
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Depositing User: Zhijun Yang
Date Deposited: 01 Feb 2022 13:00
Last Modified: 29 May 2022 17:40
URI: https://eprints.mdx.ac.uk/id/eprint/34630

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