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ISSN

2424-8460(Online)

2251-2608(Print)

Article Processing Charges (APCs)

US$800

Publication Frequency

Quarterly

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Published

2025-01-06

Issue

Vol 11 No 4 (2024): Published

Section

Articles

Research on artificial intelligence-driven internet payment risk control system

Jiaxin Zheng

Beihang University (BUAA) Al Quds Dist


DOI: https://doi.org/10.59429/esta.v11i4.8482


Keywords: Internet payment; Risk control; Artificial intelligence


Abstract

With the popularization of digital payment, the importance of Internet payment risk management has become more and more significant. This paper begins with the principles and challenges of traditional Internet payment risk control systems and analyzes the limitations of existing systems in dealing with new payment frauds. Traditional methods rely on rules and pattern recognition, while emerging fraud technologies and complex financial environments have made these methods much less effective. The article further explores the application of AI technologies in risk control systems, especially how machine learning and deep learning can effectively improve the accuracy and efficiency of risk control. Through case studies, it demonstrates the process of AI wind control system in actual deployment and its significant advantages in detecting fraud and reducing the misjudgment rate. Finally, the article discusses the future development direction and potential application areas of AI risk control systems, pointing out their significant value in improving risk management capabilities and shortening response time.


References

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[3] Sebők M, Ring O, Kis G M, et al.The geopolitics of vaccine media representation in Orbán’s Hungary—an AI-supported sentiment analysis [J].Journal of Computational Social Science, 2024, (prepublish): 1-24.

[4] Akutay S, Kaçmaz Y H ,Kahraman H .The effect of artificial intelligence supported case analysis on nursing students’ case management performance and satisfaction: A randomized controlled trial[J].Nurse Education in Practice, 2024, 80104142-104142.

[5] Kara K ,Ergin A E , Yalçın C G , et al. Sustainable brand logo selection using an AI-Supported PFWENSLO-ARLON hybrid method [J].Expert Systems With Applications, 2025,260125382-125382.



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