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Vol. 6 No. 1 (2025)
2025 Edition

Articles

AI-Driven Financial Risk Assessment and Anomaly Detection in Cross-Border Transactions: A Comprehensive Framework for Economic Security

Published 2025-05-12

  • Dai Li

Dai Li
Master Management science and engineering, Beijing Institute of Technology, Beijing, China

Abstract

The increasing complexity of global financial systems and cross-border transactions has created unprecedented challenges for risk assessment and anomaly detection. This paper presents a comprehensive framework that leverages artificial intelligence technologies to enhance financial risk assessment capabilities in cross-border transaction monitoring. Our research integrates advanced machine learning algorithms, including reinforcement learning, deep neural networks, and graph-based models, to develop a multi-layered approach for detecting suspicious financial activities. The proposed framework addresses critical gaps in current anti-money laundering systems by incorporating behavioral economics principles and real-time processing capabilities. Through extensive analysis of high-frequency trading data, credit default swap markets, and supply chain dependencies, we demonstrate the effectiveness of AI-driven approaches in identifying complex financial anomalies. Our methodology combines traditional risk indicators with emerging technologies such as federated learning and edge computing to create a robust detection system. The framework's architecture supports scalable deployment across multiple financial institutions while maintaining privacy and regulatory compliance. Experimental results indicate significant improvements in detection accuracy and reduction in false positive rates compared to conventional methods. The research contributes to the evolving landscape of financial technology by providing actionable insights for economic security policy development.

Keywords

artificial intelligence, financial risk assessment, cross-border transactions, anomaly detection

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