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

Nadia Pocher (), Mirko Zichichi (), Fabio Merizzi (), Muhammad Zohaib Shafiq () and Stefano Ferretti ()
Additional contact information
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

Electronic Markets, 2023, vol. 33, issue 1, No 37, 17 pages

Abstract: 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.

Keywords: Blockchain technology; Financial technology; Network forensics; Graph analysis; AML/CFT (search for similar items in EconPapers)
JEL-codes: G18 O33 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s12525-023-00654-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:elmark:v:33:y:2023:i:1:d:10.1007_s12525-023-00654-3

Ordering information: This journal article can be ordered from
http://www.springer. ... ystems/journal/12525

DOI: 10.1007/s12525-023-00654-3

Access Statistics for this article

Electronic Markets is currently edited by Rainer Alt and Hans-Dieter Zimmermann

More articles in Electronic Markets from Springer, IIM University of St. Gallen
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().