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Detecting anomalous cryptocurrency transactions: An AML/CFT application of machine learning-based forensics

Author

Listed:
  • Nadia Pocher

    (Universitat Autònoma de Barcelona)

  • Mirko Zichichi

    (University of Bologna)

  • Fabio Merizzi

    (University of Bologna)

  • Muhammad Zohaib Shafiq

    (University of Bologna)

  • Stefano Ferretti

    (University of Urbino)

Abstract

In shaping the Internet of Money, the application of blockchain and distributed ledger technologies (DLTs) to the financial sector triggered regulatory concerns. Notably, while the user anonymity enabled in this field may safeguard privacy and data protection, the lack of identifiability hinders accountability and challenges the fight against money laundering and the financing of terrorism and proliferation (AML/CFT). As law enforcement agencies and the private sector apply forensics to track crypto transfers across ecosystems that are socio-technical in nature, this paper focuses on the growing relevance of these techniques in a domain where their deployment impacts the traits and evolution of the sphere. In particular, this work offers contextualized insights into the application of methods of machine learning and transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques. The modeling of blockchain transactions as a complex network suggests that the use of graph-based data analysis methods can help classify transactions and identify illicit ones. Indeed, this work shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN outperform other classic approaches and GAT are applied for the first time to detect anomalies in Bitcoin. Ultimately, the paper upholds the value of public–private synergies to devise forensic strategies conscious of the spirit of explainability and data openness.

Suggested Citation

  • Nadia Pocher & Mirko Zichichi & Fabio Merizzi & Muhammad Zohaib Shafiq & Stefano Ferretti, 2023. "Detecting anomalous cryptocurrency transactions: An AML/CFT application of machine learning-based forensics," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-17, December.
  • Handle: RePEc:spr:elmark:v:33:y:2023:i:1:d:10.1007_s12525-023-00654-3
    DOI: 10.1007/s12525-023-00654-3
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    References listed on IDEAS

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    1. Micha Ober & Stefan Katzenbeisser & Kay Hamacher, 2013. "Structure and Anonymity of the Bitcoin Transaction Graph," Future Internet, MDPI, vol. 5(2), pages 1-14, May.
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    Cited by:

    1. Junliang Luo & Stefan Kitzler & Pietro Saggese, 2024. "Investigating Similarities Across Decentralized Financial (DeFi) Services," Papers 2404.00034, arXiv.org.

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    More about this item

    Keywords

    Blockchain technology; Financial technology; Network forensics; Graph analysis; AML/CFT;
    All these keywords.

    JEL classification:

    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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