Real-time Early Warning of Trading Behavior Anomalies in Financial Markets: An AI-driven Approach

Boyang Dong; Toan Khang Trinh

doi:10.70393/6a6574626d.323838

OPEN ACCESS |Research Article ||17 April 2025

Real-time Early Warning of Trading Behavior Anomalies in Financial Markets: An AI-driven Approach

Boyang Dong

University of Chicago

maxxxlee090@gmail.com

Master of Science in Financial Mathematics, University of Chicago, IL, USA.

Toan Khang Trinh

California State University Long Beach

Computer Science, California State University Long Beach, CA, USA.

* Corresponding Author¹: Boyang Dong, E-Mail: maxxxlee090@gmail.com

Publication

Accepted 2025 April 18 ; Published 2025 April 17

Journal of Economic Theory and Business Management, 2025, 2(2), 3006-4953.

Abstract

This paper introduces a novel AI-driven approach for real-time early warning of trading behavior anomalies in financial markets. The proposed system integrates advanced deep learning architectures with traditional statistical methods to enhance detection accuracy and processing efficiency. Our framework employs a multi-layered neural network design optimized for high-frequency trading pattern recognition, incorporating feature extraction mechanisms specifically calibrated for financial market data streams. The system demonstrates exceptional performance, achieving a 97.5% detection rate for known trading anomalies while maintaining false positive rates below 1%. Performance evaluation confirms the system's ability to process approximately 150,000 transactions per second with average latencies of 15 milliseconds. Comprehensive testing against 24 months of historical market data validates the system's effectiveness across diverse market conditions, including high volatility and low liquidity scenarios. Comparative analysis reveals significant performance improvements over conventional surveillance methods, with detection accuracy increasing by 28% and processing efficiency improving by 45%. The system's adaptive learning capabilities ensure continuous evolution based on emerging trading patterns. Experimental results confirm robust performance across different market sectors, including stress-tested environments and cross-asset scenarios. This research advances market surveillance technology by establishing a new benchmark for real-time anomaly detection in complex financial ecosystems.

Keywords

Trading Behavior Analytics , Machine Learning , Market Surveillance , Real-time Anomaly Detection .

Metadata

DOI:

10.70393/6a6574626d.323838

ARK:

ark:/40704/JETBM.v2n2a03

Pages: 14-23

References: 47

Disciplines: Finance

Subjects: Investment Banking

Cite This Article

APA Style

Dong, B. & Trinh, T.K. (2025). Real-time early warning of trading behavior anomalies in financial markets: an ai-driven approach. Journal of Economic Theory and Business Management, 2(2), 14-23. https://doi.org/10.70393/6a6574626d.323838

Acknowledgments

I would like to extend my sincere gratitude to Enmiao Feng, Yizhe Chen, and Zhipeng Ling for their groundbreaking research[46] on secure resource allocation optimization using deep reinforcement learning. Their innovative approach to cloud computing security and resource management has provided invaluable insights that have significantly influenced the development of my research methodology in AI-driven market surveillance systems. I am deeply grateful to Xiaowen Ma and Shukai Fan for their pioneering work[47] on cross-national customer prediction models using LSTM-Attention mechanisms. Their sophisticated application of deep learning techniques in predictive analytics has greatly enhanced my understanding of pattern recognition in complex data streams and has been instrumental in shaping the architectural design of my research project.

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

1.

Sun, R. (2021, September). Analysis on the application of artificial intelligence in marketing. In 2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI) (pp. 669-672). IEEE.

2.

Ogundipe, D. O., Babatunde, S. O., & Abaku, E. A. (2024). AI and product management: A theoretical overview from idea to market. International Journal of Management & Entrepreneurship Research, 6(3), 950-969.

3.

Gui, Y. (2023, February). Enterprise Accounting Risk Early Warning Model Based on Artificial Intelligence System Economics. In 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-5). IEEE.

4.

Dahiya, V. K., Dahiya, R., Dev, R., Sharma, A., Sharma, A., Kiran, P. N., ... & Yadav, S. (2024, October). Exploring the Role of Artificial Intelligence in Predictive Analytics for Financial Markets. In 2024 International Conference on Computing, Sciences and Communications (ICCSC) (pp. 1-5). IEEE.

5.

Bhardwaj, A., Ranjan, O., Biswas, S., Gupta, L., Chanti, Y., & Sharma, M. (2024, March). Risk Assessment and Management in Stock Trading using Artificial Intelligence. In 2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL) (pp. 138-145). IEEE.

6.

Xia, S., Zhu, Y., Zheng, S., Lu, T., & Ke, X. (2024). A Deep Learning-based Model for P2P Microloan Default Risk Prediction. International Journal of Innovative Research in Engineering and Management, 11(5), 110-120.

7.

Li, S., Xu, H., Lu, T., Cao, G., & Zhang, X. (2024). Emerging Technologies in Finance: Revolutionizing Investment Strategies and Tax Management in the Digital Era. Management Journal for Advanced Research, 4(4), 35-49.

8.

Liu, Y., Xu, Y., & Zhou, S. (2024). Enhancing User Experience through Machine Learning-Based Personalized Recommendation Systems: Behavior Data-Driven UI Design. Authorea Preprints.

9.

Xu, Y., Liu, Y., Wu, J., & Zhan, X. (2024). Privacy by Design in Machine Learning Data Collection: An Experiment on Enhancing User Experience. Applied and Computational Engineering, 97, 64-68.

10.

Xu, X., Xu, Z., Yu, P., & Wang, J. (2025). Enhancing User Intent for Recommendation Systems via Large Language Models. Preprints.

11.

Li, L., Xiong, K., Wang, G., & Shi, J. (2024). AI-Enhanced Security for Large-Scale Kubernetes Clusters: Advanced Defense and Authentication for National Cloud Infrastructure. Journal of Theory and Practice of Engineering Science, 4(12), 33-47.

12.

Yu, P., Xu, X., & Wang, J. (2024). Applications of Large Language Models in Multimodal Learning. Journal of Computer Technology and Applied Mathematics, 1(4), 108-116.

13.

Huang, T., Xu, Z., Yu, P., Yi, J., & Xu, X. (2025). A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit. arXiv preprint arXiv:2502.09097.

14.

Weng, J., Jiang, X., & Chen, Y. (2024). Real-time Squat Pose Assessment and Injury Risk Prediction Based on Enhanced Temporal Convolutional Neural Networks.

15.

Xu, X., Yu, P., Xu, Z., & Wang, J. (2025). A hybrid attention framework for fake news detection with large language models. arXiv preprint arXiv:2501.11967.

16.

Bi, W., Trinh, T. K., & Fan, S. (2024). Machine Learning-Based Pattern Recognition for Anti-Money Laundering in Banking Systems. Journal of Advanced Computing Systems, 4(11), 30-41.

17.

Shen, Q., Zhang, Y., & Xi, Y. (2024). Deep Learning-Based Investment Risk Assessment Model for Distributed Photovoltaic Projects. Journal of Advanced Computing Systems, 4(3), 31-46.

18.

Chen, J., Zhang, Y., & Wang, S. (2024). Deep Reinforcement Learning-Based Optimization for IC Layout Design Rule Verification. Journal of Advanced Computing Systems, 4(3), 16-30.

19.

Ju, C. (2023). A Machine Learning Approach to Supply Chain Vulnerability Early Warning System: Evidence from US Semiconductor Industry. Journal of Advanced Computing Systems, 3(11), 21-35.

20.

Ju, C., & Ma, X. (2024). Real-time Cross-border Payment Fraud Detection Using Temporal Graph Neural Networks: A Deep Learning Approach. International Journal of Computer and Information System (IJCIS), 5(1), 103-114.

21.

Xiong, K., Wu, Z., & Jia, X. (2025). DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments. Journal of Advanced Computing Systems, 5(1), 1-17.

22.

Wang, S., Hu, C., & Jia, G. (2024). Deep Learning-Based Saliency Assessment Model for Product Placement in Video Advertisements. Journal of Advanced Computing Systems, 4(5), 27-41.

23.

Pu, Y., Chen, Y., & Fan, J. (2023). P2P Lending Default Risk Prediction Using Attention-Enhanced Graph Neural Networks. Journal of Advanced Computing Systems, 3(11), 8-20.

24.

Jin, M., Zhang, H., & Huang, D. (2024). Deep Learning-Based Early Warning Model for Continuous Glucose Monitoring Data in Diabetes Management. Integrated Journal of Science and Technology, 1(2).

25.

Ma, X., & Jiang, X. (2024). Predicting Cross-border E-commerce Purchase Behavior in Organic Products: A Machine Learning Approach Integrating Cultural Dimensions and Digital Footprints. International Journal of Computer and Information System (IJCIS), 5(1), 91-102.

26.

Xiong, K., Cao, G., Jin, M., & Ye, B. (2024). A Multi-modal Deep Learning Approach for Predicting Type 2 Diabetes Complications: Early Warning System Design and Implementation.

27.

Fan, J., Trinh, T. K., & Zhang, H. (2024). Deep Learning-Based Transfer Pricing Anomaly Detection and Risk Alert System for Pharmaceutical Companies: A Data Security-Oriented Approach. Journal of Advanced Computing Systems, 4(2), 1-14.

28.

Xi, Y., Jia, X., & Zhang, H. (2024). Real-time Multimodal Route Optimization and Anomaly Detection for Cross-border Logistics Using Deep Reinforcement Learning. International Journal of Computer and Information System (IJCIS), 5(2), 102-114.

29.

Chen, J., & Wang, S. (2024). A Deep Reinforcement Learning Approach for Network-on-Chip Layout Verification and Route Optimization. International Journal of Computer and Information System (IJCIS), 5(1), 67-78.

30.

Jia, X., Zhang, H., Hu, C., & Jia, G. (2024). Joint Enhancement of Historical News Video Quality Using Modified Conditional GANs: A Dual-Stream Approach for Video and Audio Restoration. International Journal of Computer and Information System (IJCIS), 5(1), 79-90.

31.

Zhang, H., Jia, X., & Chen, C. (2025). Deep Learning-Based Real-Time Data Quality Assessment and Anomaly Detection for Large-Scale Distributed Data Streams.

32.

Wang, J., Zhao, Q., & Xi, Y. (2025). Cross-lingual Search Intent Understanding Framework Based on Multi-modal User Behavior. Annals of Applied Sciences, 6(1).

33.

Ju, C., Shen, Q., & Ni, X. (2024). Leveraging LSTM Neural Networks for Stock Price Prediction and Trading Strategy Optimization in Financial Markets. Applied and Computational Engineering, 112, 47-53.

34.

Ju, C., Liu, Y., & Shu, M. (2024). Performance evaluation of supply chain disruption risk prediction models in healthcare: A multi-source data analysis.

35.

Ma, D., Jin, M., Zhou, Z., Wu, J., & Liu, Y. (2024). Deep Learning-Based ADL Assessment and Personalized Care Planning Optimization in Adult Day Health Center. Applied and Computational Engineering, 118, 14-22.

36.

Wei, M., Wang, S., Pu, Y., & Wu, J. (2024). Multi-Agent Reinforcement Learning for High-Frequency Trading Strategy Optimization. Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930), 2(1), 109-124.

37.

Wen, X., Shen, Q., Wang, S., & Zhang, H. (2024). Leveraging AI and Machine Learning Models for Enhanced Efficiency in Renewable Energy Systems. Applied and Computational Engineering, 96, 107-112.

38.

Xu,J.;Chen,H.;Xiao,X.;Zhao,M.;Liu,B. (2025).Gesture Object Detection and Recognition Based on YOLOv11.Applied and Computational Engineering,133,81-89.

39.

Chen, H., Shen, Z., Wang, Y., & Xu, J. (2024). Threat Detection Driven by Artificial Intelligence: Enhancing Cybersecurity with Machine Learning Algorithms.

40.

Xu, J., Wang, Y., Chen, H., & Shen, Z. (2025). Adversarial Machine Learning in Cybersecurity: Attacks and Defenses. International Journal of Management Science Research, 8(2), 26-33.

41.

Liang, X., & Chen, H. (2024, July). One cloud subscription-based software license management and protection mechanism. In Proceedings of the 2024 International Conference on Image Processing, Intelligent Control and Computer Engineering (pp. 199-203).

42.

Huang, T., Yi, J., Yu, P., & Xu, X. (2025). Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies.

43.

Wang, J., Xu, X., Yu, P., & Xu, Z. (2025). Hierarchical Multi-Stage BERT Fusion Framework with Dual Attention for Enhanced Cyberbullying Detection in Social Media.

44.

Yi, J., Xu, Z., Huang, T., & Yu, P. (2025). Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions. arXiv preprint arXiv:2502.00339.

45.

Yu, P., Xu, Z., Wang, J., & Xu, X. (2025). The Application of Large Language Models in Recommendation Systems. arXiv preprint arXiv:2501.02178.

46.

Chen, Y., Feng, E., & Ling, Z. (2024). Secure Resource Allocation Optimization in Cloud Computing Using Deep Reinforcement Learning. Journal of Advanced Computing Systems, 4(11), 15-29.

47.

Ma, X., & Fan, S. (2024). Research on Cross-national Customer Churn Prediction Model for Biopharmaceutical Products Based on LSTM-Attention Mechanism. Academia Nexus Journal, 3(3).

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