
JIEAS OPEN ACCESS
Journal of Industrial Engineering and Applied Science
ISSN:3005-608X (print) | ISSN:3005-6071 (online) | Publication Frequency: Bimonthly
Combining Blockchain and AI to Optimize the Intelligent Risk Control Mechanism in Decentralized Finance
* Corresponding Author1: Tianzuo Zhang, E-Mail: tianzuoz@usc.edu
Publication
Accepted 2025 March 20 ; Published 2025 April 1
Journal of Industrial Engineering and Applied Science, 2025, 3(2), 3005-6071.
Abstract
This study explores the optimized application of combining blockchain (Blockchain) and artificial intelligence (AI) in the intelligent risk control of decentralized finance (DeFi). Although the decentralization and transparency of DeFi have driven financial innovation, they have also introduced risks related to market manipulation, smart contract vulnerabilities, and liquidity. Traditional centralized risk control approaches struggle to adapt. This research proposes a blockchain+AI-based intelligent risk control framework. Blockchain’s tamper-resistance enhances transaction security, while AI’s intelligent learning capabilities improve risk identification. Experimental results show that this model outperforms traditional solutions in detection accuracy (94.1%), false alarm rate (2.1%), and detection latency (180ms), and it remains robust under high market volatility. The findings suggest that combining blockchain and AI can effectively strengthen DeFi risk control, enhance system transparency and security, and provide theoretical and practical directions for future intelligent and automated risk management.
Keywords
Blockchain , Artificial Intelligence , Decentralized Finance (DeFi) , Risk Management , Intelligent Risk Control , Smart Contracts .
Metadata
Pages: 26-32
References: 11
Disciplines: Artificial Intelligence Technology
Subjects: Cybersecurity
Cite This Article
APA Style
Zhang, T. (2025). Combining blockchain and ai to optimize the intelligent risk control mechanism in decentralized finance. Journal of Industrial Engineering and Applied Science, 3(2), 26-32. https://doi.org/10.70393/6a69656173.323739
Acknowledgments
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
FUNDING
Not applicable.
INSTITUTIONAL REVIEW BOARD STATEMENT
Not applicable.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
INFORMED CONSENT STATEMENT
Not applicable.
CONFLICT OF INTEREST
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
AUTHOR CONTRIBUTIONS
Not applicable.
References
PUBLISHER'S NOTE
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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