A Compliance Service Mode Selection Model Based on Machine Learning Algorithms

Zhongkai Niu; Weilin Li; Zhuyin Yan; Huali Chen

doi:10.54254/2755-2721/2025.25153

Applied and Computational EngineeringOpen access

A Compliance Service Mode Selection Model Based on Machine Learning Algorithms

Research Article

Open Access

A Compliance Service Mode Selection Model Based on Machine Learning Algorithms

Zhongkai Niu ¹, Weilin Li ², Zhuyin Yan ³, Huali Chen ^4*

¹ School of Law, Guangzhou Xinhua University, Dongguan 523133, China

² School of Law, Guangzhou Xinhua University, Dongguan 523133, China

³ Taishan College, Shandong University, Jinan 250100, China

⁴ School of Law, Guangzhou Xinhua University, Dongguan 523133, China

^*Corresponding author: 375921677@qq.com

Published on 20 July 2025

ACE Vol.173

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-231-7

ISBN (Online): 978-1-80590-232-4

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Abstract

This study is based on multidimensional compliance feature data of enterprises, and deeply explores and constructs an intelligent selection model for compliance service modes based on machine learning algorithms. By systematically comparing the performance of decision trees, random forests, support vector machines (SVM), BP neural networks, and the non-linear weight particle swarm optimization based support vector machine (NWPSO-SVM) classification model proposed in this study on the same dataset, the empirical results clearly show that the NWPSO-SVM model exhibits excellent and comprehensive classification ability in compliance service mode selection tasks. The model achieved the best results in all key evaluation metrics, with an accuracy of 0.894, a recall of 0.894, a precision of 0.886, and a stable F1 Score of 0.886. This series of significantly leading indicator values not only confirms the high accuracy of the model's prediction results. By utilizing this model for precise analysis and pattern prediction of enterprise compliance characteristics, potential compliance risk points can be identified proactively at the beginning of data service launch or product design, and appropriate service patterns can be matched. This enables compliance requirements to be more intelligently and efficiently embedded at the source of business processes, achieving a transition from passive response to active prevention, effectively improving the accuracy and effectiveness of pre regulation, reducing compliance costs and violation risks in the later stage, and laying a solid technical foundation for building a secure, reliable, and trustworthy data service ecosystem. In summary, the NWPSO-SVM model has been proven to be an ideal tool for intelligent and precise selection of enterprise compliance service models due to its significantly superior comprehensive performance.

Keywords:

Compliance service mode, non-linear weighted particle swarm algorithm, support vector machine.

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References

[4]. C R K V , A J C R V V , Michel O .Risk Adjustment in Health Insurance Markets: Do Not Overlook the Real Healthy. [J].Medical care, 2023,

[5]. Siegfried G , Juliane T , Stefanie S , et al.Decreasing COPD-related incidences and hospital admissions in a German health insurance population [J].Scientific Reports, 2023, 13(1): 21293-21293.

[7]. S. A M , Katia B , Michelle K , et al.Access to Specialized Care Across the Lifespan in Tetralogy of Fallot [J].CJC Pediatric and Congenital Heart Disease, 2023, 2(6PA): 267-282.

[8]. Jungtaek L .Effects of private health insurance on healthcare services during the MERS Pandemic: Evidence from Korea [J].Heliyon, 2023, 9(12): e22241-e22241.

[9]. Jiang, John Xuefeng, et al. "Factors associated with compliance to the hospital price transparency final rule: a national landscape study." Journal of general internal medicine (2022): 1-8.

[10]. Wang, Yuqing, and Xiao Yang. "Machine Learning-Based Cloud Computing Compliance Process Automation." arXiv preprint arXiv: 2502.16344 (2025).

Cite this article

Niu,Z.;Li,W.;Yan,Z.;Chen,H. (2025). A Compliance Service Mode Selection Model Based on Machine Learning Algorithms. Applied and Computational Engineering,173,57-63.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of the 7th International Conference on Computing and Data Science

ISBN: 978-1-80590-231-7(Print) / 978-1-80590-232-4(Online)

Editor: Marwan Omar

Conference website: https://2025.confcds.org/

Conference date: 25 September 2025

Series: Applied and Computational Engineering

Volume number: Vol.173

ISSN: 2755-2721(Print) / 2755-273X(Online)

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