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Research Article

A Credit Risk Model with Small Sample Data Based on G-XGBoost

ORCID Icon, ORCID Icon, ORCID Icon &
Pages 1550-1566 | Received 07 Aug 2020, Accepted 27 Sep 2021, Published online: 28 Oct 2021

References

  • Andrade, F. W. M., and A. L. Sicsú. 2008. A credit risk model for consumer loan portfolios. Latin American Business Review 8 (3):75–91. doi:https://doi.org/10.1080/10978520802035430.
  • Antoniou, A., A. Storkey, and H. Edwards. 2018. Data augmentation generative adversarial networks, 1-14. [online] Available: https://arxiv.org/abs/1711.04340.
  • Boz, Z., D. Gunnec, S. I. Birbil, and M. K. Öztürk. 2018. Reassessment and monitoring of loan applications with machine learning. Applied Artificial Intelligence 32 (9–10):939–55. doi:https://doi.org/10.1080/08839514.2018.1525517.
  • Chen, T. Q., and C. Guestrin. 2016. Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, 785–94. [online] Available: https://arxiv.org/abs/1603.02754.
  • Chen, Y. Z., Y. S. Wang, D. Kirschen, and B. Zhang. 2018. Model-free renewable scenario generation using generative adversarial networks. IEEE Transactions on Power Systems 33 (3):3265–75. doi:https://doi.org/10.1109/TPWRS.2018.2794541.
  • Cheng, Z. 2014. The key to the healthy development of internet finance lies in risk management. Enterprise Reform and Management 9:144.
  • Chi, G. T., M. D. Pan, and F. Qi. 2014. A credit rating model for analyzing bank customers based on small sample. The Journal of Quantitative & Technical Economics 6:102–16.
  • Feng, G. Q., D. L. Cui, K. Q. Zhu, and Q. Zhang. 2019. Research of modeling with small sample for complex problem. Control Engineering of China 26 (11):2013–18.
  • Frid-Adar, M., E. Klang, M. Amitai, J. Goldberger, and H. Greenspan. 2018. Synthetic data augmentation using GAN for improved liver lesion classification. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA, 289–93. doi: https://doi.org/10.1109/ISBI.2018.8363576.
  • Friedman, J. H. 2001. Greedy function approximation: A gradient boosting machine. Annals of Statistics 29 (5):1189–232. doi:https://doi.org/10.1214/aos/1013203451.
  • Gao, Q., and Z. H. Jiang. 2019. Amplification of small sample library based on GAN equivalent model. Electrical Measurement & Instrumentation 56 (6):76-81.
  • Goodfellow, I. J., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. Generative Adversarial Networks, Advances in Neural Information Processing Systems, 3:2672–80. [online] Available: https://arxiv.org/abs/1406.2661.
  • Gupta, A., J. Johnson, F. F. Li, S. Savarese, and A. Alahi. 2018. Social GAN: Socially acceptable trajectories With Generative Adversarial Networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2255–64. Salt Lake City, UT. doi: https://doi.org/10.1109/CVPR.2018.00240.
  • Hindistan, Y. S., B. A. Aiyakogu, A. M. Rezaeinazhad, H. E. Korkmaz, and H. Dag. 2019. Alternative credit scoring and classification employing machine learning techniques on a big data platform. 2019 4th International Conference on Computer Science and Engineering (UBMK), Samsun, Turkey, 1–4. doi: https://doi.org/10.1109/UBMK.2019.8907113.
  • Jin, P. H. 2003. Medical statistical method, vol. 46. Shanghai: Fudan University Press.
  • Kruppa, J., A. Schwarz, G. Arminger, and A. Ziegler. 2013. Consumer credit risk: Individual probability estimates using machine learning. Expert Systems with Applications 40 (13):5125–31. doi:https://doi.org/10.1016/j.eswa.2013.03.019.
  • Lee, Y. H., and S. Z. Cho. 2020. Design of semantic-based colorization of graphical user interface through conditional generative adversarial nets. International Journal of Human–Computer Interaction 36 (8):699–708. doi:https://doi.org/10.1080/10447318.2019.1680921.
  • Li, C. Y. 2019a. The influence of sample size change on the prediction accuracy of shanghai stock index. Henan Science and Technology 28:8–10.
  • Li, Y. 2019b. Credit risk prediction based on machine learning methods. 2019 14th International Conference on Computer Science & Education (ICCSE), Toronto, Canada, 1011–13.
  • Li, Z. J., F. Ju, C. B. Xiu, and G. H. Qiao. 2016. The construction of bank credit risk small sample rating model. Statistics and Decision 453 (9):41–45.
  • Liu, Z. H., Z. G. Huang, and H. L. Xie. 2019. Is risk management with big data effective? —Comparison and analysis based on statistics score card and machine learning model. Statistics & Information Forum 34 (9): 18-26.
  • Luo, C., D. Wu, and D. Wu. 2016. A deep learning approach for credit scoring using credit default swaps. Engineering Applications of Artificial Intelligence 65 (32). doi: https://doi.org/10.1016/j.engappai.2016.12.002.
  • Ma, L. 2019. Research on innovation of financing model and risk management of small and micro enterprises under the background of internet finance. Institute of Management Science and Industrial Engineering (ISMEEM), Hanoi, Vietnam, 125–29.
  • Mamdouh, R. 2011. Credit risk scorecards: Development and implementation using SAS, USA: Lulu.com.
  • Mehta, K., Z. Kobti, K. A. Pfaff, and S. Fox. 2019. Data augmentation using CA evolved GANs. 2019 IEEE Symposium on Computers and Communications (ISCC), Barcelona, Spain, 1087–92. doi: https://doi.org/10.1109/ISCC47284.2019.8969638.
  • Mirza, M., and S. Osindero. 2014. Conditional Generative Adversarial Nets. [online] Available: https://arxiv.org/abs/1411.1784.
  • Nedellec, C., J. Correia, J. Ferreira, and E. Costa. 1994. Machine learning goes to the bank. Applied Artificial Intelligence 8 (4):593–615. doi:https://doi.org/10.1080/08839519408945461.
  • Qiu, W. Y. 2019. Credit risk prediction in an imbalanced social lending environment based on XGBoost. 2019 5th International Conference on Big Data and Information Analytics (BigDIA), 150–56. Kunming, China. doi: https://doi.org/10.1109/BigDIA.2019.8802747.
  • Radford, A., L. Metz, and S. Chintala. 2016. Unsupervised representation learning with deep convolutional generative adversarial networks. Computer Science, Proc. Int. Conf. Learn. Represent., San Juan, Puerto Rico [online] Available: https://arxiv.org/abs/1511.06434.
  • Rahbar, M., M. Mahdavinejad, M. Bemanian, A. H. Davaie Markazi, and L. Hovestadt. 2019. Generating synthetic space allocation probability layouts based on trained conditional-GANs. Applied Artificial Intelligence 33 (8):689–705. doi:https://doi.org/10.1080/08839514.2019.1592919.
  • Ratliff, L. J., S. A. Burden, and S. S. Sastry. 2016. On the characterization of local nash equilibria in continuous games. IEEE Transactions on Automatic Control 61 (8):2301–07. doi:https://doi.org/10.1109/TAC.2016.2583518.
  • Shukla, U. P., and S. J. Nanda. 2019. Designing of a risk assessment model for issuing credit card using parallel social spider algorithm. Applied Artificial Intelligence 33 (3):191–207. doi:https://doi.org/10.1080/08839514.2018.1537229.
  • Tran, K., T. Duong, and Q. Ho. 2016. A combination of genetic programming and deep learning. Future Technologies Conference (FTC), San Francisco, CA, USA, 145–49. doi: https://doi.org/10.1109/FTC.2016.7821603.
  • Wang, B., Y. Kong, T. T. Zhang, D. P. Liu, and L. J. Ning. 2019a. Integration of unsupervised and supervised machine learning algorithms for credit risk assessment. Expert Systems with Applications 128:301–15. doi:https://doi.org/10.1016/j.eswa.2019.02.033.
  • Wang, C. Y., C. Xu, X. Yao, and D. C. Tao. 2019b. Evolutionary generative adversarial networks. IEEE Transactions on Evolutionary Computation. [online] Available: https://arxiv.org/abs/1803.00657
  • Wang, G. X., W. X. Kang, Q. X. Wu, Z. Y. Wang, and J. B. Gao. 2018. Generative adversarial network (GAN) based data augmentation for palmprint recognition. 2018 Digital Image Computing: Techniques and Applications (DICTA), 1–7. Canberra, Australia. doi: https://doi.org/10.1109/DICTA.2018.8615782.
  • Wang, H. X., J. D. Zhong, D. F. Zhang, and X. Y. Zou. 2017. A new classification algorithm for the bank customer credit rating. 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) IEEE, Doha, Qatar, 143–48. doi: https://doi.org/10.1109/ICACI.2017.7974499.
  • Wiese, M., R. Knobloch, R. Korn, and P. Kretschmer. 2020. Quant GANs: Deep generation of financial time series. Quantitative Finance 1–22. doi:https://doi.org/10.1080/14697688.2020.1730426.
  • Wiginton, J. 1980. A note on the comparison of logit and discriminant models of consumer credit behavior. The Journal of Financial and Quantitative Analysis 15 (3):757–70. doi:https://doi.org/10.2307/2330408.
  • Zhang, D., H. Huang, Q. Chen, and Y. Jiang. 2007. A comparison study of credit scoring models. Third International Conference of Natural Computation, Haikou, China.
  • Zheng, L. C. 2014. Internet finance in China: Models, impact, nature and the risks. International Economic Review 5:103–18.