Real-time Cross-border Payment Fraud Detection Using Temporal Graph Neural Networks: A Deep Learning Approach
Paper introduces a Temporal Graph Neural Network for real-time, cross-border payment fraud detection, emphasizing advanced feature engineering and computational efficiency.
What it examines
This study introduces a novel Temporal Graph Neural Network for real-time cross-border payment fraud detection. It combines spatial and temporal features with dynamic graph modeling and multi-head attention to capture evolving fraud patterns, addressing limitations in traditional static methods and ensuring efficient processing in international financial systems.
What it concludes
The results show high detection accuracy and computational efficiency, significantly reducing false positives. This research can support international payment monitoring, risk management, and fraud prevention. Future work may refine feature extraction and extend the model to other financial fraud areas, enhancing global transaction security.
Evidence objects
A state-of-the-art Temporal Graph Neural Network model integrates spatial and temporal features to achieve fraud detection accuracy of 99.24% and 98.76%, while reducing false positives by 37% in cross-border payments.
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Innovative multi-head attention mechanisms and feature engineering pipelines underpin the models breakthroughs, dynamic graph construction, temporal edge weighting, and real-time node feature extraction for evolving fraud patterns in international transactions.
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Extensive experiments on large-scale transaction datasets reveal the models robustness amid variable transaction volumes and low computational latency, though the study raises concerns about scalability and data quality among jurisdictions.
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This work introduces a Temporal Graph Neural Network for cross-border payment fraud, combining innovative attention mechanisms and real-time processing for enhanced detection efficacy. Although incremental relative to established AI trading techniques, it presents a novel perspective and method, engaging readers with its approach and significant impact on financial security systems.
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Raw abstract and provenance
- … to increased risks in the financial system, especially in … ) approach for real-time financial fraud detection. The … patterns while maintaining low computational overhead. The …
Source row: 1654 · abstract type: snippet