Abstract
Green credit plays a crucial role in promoting green transformation of enterprises and advancing social sustainable development. However, the current green credit rating disclosure system lacks data sharing between different institutions, leading to inconsistencies in evaluation results. To address this issue, this study proposes a green credit risk control model based on SecureBoost and an Improved-TCA algorithm. The proposed model combines vertical federated learning result with feature transfer to protect the privacy of participants in different datasets and analyzing the experimental results of vertical federated learning using SHAP values. We proposes improved TCA, which combines the BDA algorithm with the TCA algorithm, and improves the TCA algorithm by setting different weight ratios to comprehensively integrate the advantages of both algorithms to address the issue of significantly different sample distribution quantities in certain data set applications. We proved that the improved TCA algorithm combined with secureBoost has a better prediction result in the multi-classification credit evaluation scenario.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, W., Zhang, Y.X.: Review of measuring and evaluating green credit and its influence literature. Cooperation Econ. Technol. 697(02), 56–58 (2023). https://doi.org/10.13665/j.cnki.hzjjykj.2023.02.045
Zhao, Q.X.: The impact of green credit on the operational performance and risk-taking of commercial banks. China Price 408(04), 77–80 (2023)
Shu, L.M., Liao, J.H., **e, Z.: Green credit policy and enterprise green innovation: empirical evidence based on the perspective of green industry. Financ. Econ. Res. 38(02), 144–160 (2023)
Long, M., Wang, J., Ding, G., et al.: Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5147–5154 (2014)
Yang, Q., Liu, Y., Chen, T., et al.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), 1–19 (2019)
Pan, S.J., Tsang, I.W., Kwok, J.T., et al.: Domain adaptation via transfer component analysis. In: International Joint Conference on Artificial Intelligence. Morgan Kaufmann Publishers Inc., pp. 1186–1191 (2009)
Wang, J., Chen, Y., Hao, S., et al.: Balanced distribution adaptation for transfer learning. IEEE Int. Conf. Data Min., 1125–1130. IEEE (2017)
Cheng, K., Fan, T., **, Y., et al.: SecureBoost: a lossless federated learning framework. IEEE Intell. Syst., (99), 1–1 (2021)
Merrick, L., Taly, A.: The explanation game: explaining machine learning models using shapley values. ar**v preprint ar**v:1903.00031 (2019)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. 13(1), 1–15 (2016)
Sun, Q., Amin, M., Yan, B., et al.: Transfer learning for bilingual content classification. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1531–1540
Scannapieco, M., Figotin, I., Bertino, E., et al.: Privacy preserving schema and data matching. In: ACM Sigmod International Conference on Management of Data. ACM (2007)
Gretton, A., Borgwardt, K.M., Rasch, M., et al.: A kernel two-sample test. J. Mach. Learn. Res. 13, 723–773 (2012)
Dai, L.Y., Li, C., Mao, M.H.: Heterogeneous efficiency incentive of green credit on commercial banks: an empirical study based on meta-frontier DEA framework. J. Hainan University (Social Science Edition), 1–11 (2023). https://doi.org/10.15886/j.cnki.hnus.202209.0210
Yang, Q.: Federated Learning. Electronic Industry Press, Bei**g (2020). (in Chinese)
Lundberg, S., Lee, S.I.: A unified approach to interpreting model predictions. Adv. Neural. Inf. Process. Syst. 30, 4765–4774 (2017)
Wang, J., Chen, Y., Feng, W., et al.: Transfer learning with dynamic distribution adaptation. ACM Trans. Intell. Syst. Technol. 11(1), 1–25 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, M., Yan, J., Chen, Y. (2024). Design and Implementation of a Green Credit Risk Control Model Based on SecureBoost and Improved-TCA Algorithm. In: **, H., Yu, Z., Yu, C., Zhou, X., Lu, Z., Song, X. (eds) Green, Pervasive, and Cloud Computing. GPC 2023. Lecture Notes in Computer Science, vol 14503. Springer, Singapore. https://doi.org/10.1007/978-981-99-9893-7_14
Download citation
DOI: https://doi.org/10.1007/978-981-99-9893-7_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9892-0
Online ISBN: 978-981-99-9893-7
eBook Packages: Computer ScienceComputer Science (R0)