Proceedings of the 2025 4th International Conference on Mathematical Statistics and Economic Analysis (MSEA 2025)

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Application of Machine Learning in Credit Assessment of Digital Economy Enterprises

Authors
Muhang Xin1, *, Jiahui Xu2, Bingfeng Yao3, Xiaojuan Yang4, Jiangxin Li5, Shengyang Wang6
1Tianjin University, Tianjin, 300354, China
2Central University of Finance and Economics, Beijing, 102206, China
3Minzu University of China, Beijing, 100081, China
4Central University of Finance and Economics, Beijing, 102206, China
5The University of New South Wales, Sydney, 2032, Australia
6Beijing Jiaotong University, Beijing, 100044, China
*Corresponding author. Email: 17627882752@163.com
Corresponding Author
Muhang Xin
Available Online 20 February 2026.
DOI
10.2991/978-94-6463-992-6_34How to use a DOI?
Keywords
machine learning; digital economy enterprises; credit assessment; ensemble learning; risk identification
Abstract

With the vigorous development of the digital economy, traditional credit evaluation methods can hardly meet the complex credit risk identification needs of digital economic enterprises. Machine learning, with its comprehensive data processing capabilities, offers new solutions. This research constructs a machine learning credit evaluation model for such enterprises: by analyzing corporate credit characteristics, it designs tailored parameter configurations, and integrates supervised, unsupervised and integrated learning algorithms (drawing on others’ strengths) to form a comprehensive credit evaluation technical system. Experimental results show this hybrid integrated model outperforms traditional credit scoring methods in indicators like accuracy, precision, recall, F1 value and AUC, and can clearly identify the credit risks of digital economic enterprises.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 4th International Conference on Mathematical Statistics and Economic Analysis (MSEA 2025)
Series
Advances in Economics, Business and Management Research
Publication Date
20 February 2026
ISBN
978-94-6463-992-6
ISSN
2352-5428
DOI
10.2991/978-94-6463-992-6_34How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Muhang Xin
AU  - Jiahui Xu
AU  - Bingfeng Yao
AU  - Xiaojuan Yang
AU  - Jiangxin Li
AU  - Shengyang Wang
PY  - 2026
DA  - 2026/02/20
TI  - Application of Machine Learning in Credit Assessment of Digital Economy Enterprises
BT  - Proceedings of the 2025 4th International Conference on Mathematical Statistics and Economic Analysis (MSEA 2025)
PB  - Atlantis Press
SP  - 369
EP  - 376
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-992-6_34
DO  - 10.2991/978-94-6463-992-6_34
ID  - Xin2026
ER  -