Vol. 5 No. 1 (2025): January−June 2025
Articles

Machine Learning-Based Identification of Anomalous Trading Behavior Patterns Among Asia-Pacific Investors in U.S. Securities Markets

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  • Yilun Li,
  • Shukai Fan,
  • Haozhe Wang
Yilun Li
Quantitative Finance, Washington University, MO, USA
Shukai Fan
Data Sciences., University of Michigan, MI, USA
Haozhe Wang
Operations Research, Concentrated in Financial Engineering, Cornell University, NY, USA

Published 2025-06-24

Keywords

  • Anomaly Detection,
  • Cross-Border Trading,
  • Machine Learning,
  • Financial Surveillance

How to Cite

Li, Y., Fan, S., & Wang, H. (2025). Machine Learning-Based Identification of Anomalous Trading Behavior Patterns Among Asia-Pacific Investors in U.S. Securities Markets. Spectrum of Research, 5(1). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/37

Copyright (c) 2025 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This research presents a comprehensive machine learning framework for detecting anomalous trading behaviors among Asia-Pacific investors in U.S. securities markets. Through the analysis of high-frequency trading data spanning multiple market conditions, we develop culturally-aware artificial intelligence models that enhance Anti-Money Laundering (AML) capabilities while addressing regional behavioral characteristics. Our methodology integrates temporal-contextual analytics with ensemble learning techniques, achieving superior detection accuracy compared to traditional rule-based systems. The proposed framework demonstrates significant improvements in identifying suspicious cross-border transactions while reducing false positive rates by 34.7%. Implementation of dynamic threshold adjustment mechanisms and multi-dimensional feature engineering enables real-time monitoring capabilities essential for regulatory compliance. Empirical validation using data from major Asia-Pacific economies reveals distinct behavioral patterns that traditional surveillance systems fail to capture. The research contributes to advancing regulatory technology applications in global financial markets and provides actionable insights for enhancing cross-border financial crime detection frameworks.

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