Temporal Feature-Based Suspicious Behavior Pattern Recognition in Cross-Border Securities Trading
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Abstract
Cross-border securities trading has experienced unprecedented growth, accompanied by increasingly sophisticated financial crime patterns that pose significant challenges to traditional detection systems. This paper presents a novel temporal feature-based approach for identifying suspicious behavior patterns in international securities markets. The proposed methodology integrates advanced time-series analysis with machine learning algorithms to extract meaningful temporal characteristics from multi-jurisdictional trading data. Our framework addresses the limitations of existing rule-based anti-money laundering systems by incorporating cultural and regional trading pattern variations. The experimental validation demonstrates superior performance compared to conventional approaches, achieving 94.7% accuracy in detecting suspicious cross-border trading behaviors while reducing false positive rates by 68%. The research contributes to enhanced financial market surveillance capabilities and provides regulatory authorities with more effective tools for combating international financial crime. Our temporal feature extraction methodology successfully identifies previously undetectable patterns in cross-border trading sequences, offering significant improvements in both detection precision and computational efficiency for real-world deployment scenarios.
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