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October 27, 2025Journal of Artificial Intelligence ResearchOpen Access

Blockchain Fraud Detection Using Ensemble Graph Neural Networks

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Authors

MHMuhammad Zulqurnain HaiderUniversité du Québec à MontréalTNTayyaba NoreenÉcole de Technologie SupérieureMSMahwish SalmanGovernment College University, Faisalabad

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Implication

This analysis demonstrates enhanced fraud detection in cryptocurrencies, suggesting effective frameworks for monitoring illicit transactions.

Key Points

  • Over 70% of illicit transactions detected using ensemble graph neural networks, while false alarms are kept under 1%.
  • Assessment shows a practical balance of precision and coverage for real-time anti-money laundering.
  • Utilizing the Elliptic dataset, the method combines predictions from multiple graph neural networks to improve detection capabilities.
  • Significant implications for scalable and reliable cryptocurrency fraud monitoring systems are highlighted.

Cite This Study

Haider et al. (2025) studied this question.

synapsesocial.com/papers/68ff87d8c8c50a61f2bdcbbfhttps://doi.org/10.70891/jair.2025.080018
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Towards Quantum-Ready Blockchain Fraud Detection via Ensemble Graph Neural Networks2025
  2. 2Exploring the Use of Graph Neural Networks for Blockchain Transaction Analysis and Fraud Detection2024 · 2 citations
  3. 3AI-Driven Fraud Detection in Financial Transactions with Graph Neural Networks and Anomaly Detection2024 · 27 citations
  4. 4Graph Neural Network-Based Fraud Detection In Blockchain Supply Networks2026
  5. 5Enhancing predictive models for illicit activities in the Bitcoin transaction network using advanced graph analytical techniques2024 · 2 citations