NEW AML TOOLS: ANALYZING ETHEREUM CRYPTOCURRENCY TRANSACTIONS USING A BAYESIAN CLASSIFIER

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dc.contributor.authorLyeonov, Serhiycs
dc.contributor.authorTumpach, Milošcs
dc.contributor.authorLoskorikh, Gabriellacs
dc.contributor.authorFilatova, Hannacs
dc.contributor.authorReshetniak, Yaroslavcs
dc.contributor.authorDinits, Ruslancs
dc.coverage.issue57cs
dc.coverage.volume4cs
dc.date.accessioned2025-04-04T11:56:33Z
dc.date.available2025-04-04T11:56:33Z
dc.date.issued2024-09-21cs
dc.description.abstractThe emergence of cryptocurrencies as a form of digital payments has contributed to the emergence of numerous opportunities for the implementation of effective and efficient financial transactions, however, new fraud and money laundering schemes have emerged, as the anonymity and decentralization inherent in cryptocurrencies complicate the process of monitoring transactions and control by governments and law enforcement agencies. This study aims to develop a mechanism for analyzing transactions in the Ethereum cryptocurrency using a Bayesian classifier to identify potentially suspicious transactions that may be related to terrorist financing and money laundering. The Bayesian approach makes it possible to consider the probabilistic characteristics of transactions and their interrelationships to increase the accuracy of detecting anomalous and potentially illegal transactions. For the analysis, data on transactions of the Ethereum currency from June 2020 to December 2022 were taken. The developed mechanism involves determining a set of characteristics of transaction graph nodes that identify the potential for their use in illegal financial transactions and forming intervals of their permissible values. The article presents cryptocurrency transactions as an oriented graph, with the nodes being the entities conducting transactions and the arcs being the transactions between the nodes. In assessing the risks of using cryptocurrencies in money laundering, the number/amount of transactions to and from the respective node, the balance of these transactions (absolute value), and the type of node were considered. The analysis showed that among the 100 largest nodes in the network, 11 were identified as having a << critical >> risk level, and the most closely connected nodes were identified. This methodology can be used not only to analyze the Ethereum cryptocurrency but also for other cryptocurrencies and similar networks.en
dc.formattextcs
dc.format.extent274-288cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationFinancial and Credit Activity-Problems of Theory and Practice. 2024, vol. 4, issue 57, p. 274-288.en
dc.identifier.doi10.55643/fcaptp.4.57.2024.4500cs
dc.identifier.issn2306-4994cs
dc.identifier.orcid0000-0003-3389-6803cs
dc.identifier.other197217cs
dc.identifier.researcheridABF-6094-2020cs
dc.identifier.scopus6311027000cs
dc.identifier.urihttps://hdl.handle.net/11012/250753
dc.language.isoencs
dc.publisherFintechalliance LLCcs
dc.relation.ispartofFinancial and Credit Activity-Problems of Theory and Practicecs
dc.relation.urihttps://fkd.net.ua/index.php/fkd/article/view/4500/4162cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2306-4994/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectcryptocurrencyen
dc.subjectEthereumen
dc.subjectblockchainen
dc.subjectterrorist financingen
dc.subjectmoney launderingen
dc.subjecttransaction analysisen
dc.subjectBayesian classifieren
dc.titleNEW AML TOOLS: ANALYZING ETHEREUM CRYPTOCURRENCY TRANSACTIONS USING A BAYESIAN CLASSIFIERen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-197217en
sync.item.dbtypeVAVen
sync.item.insts2025.04.04 13:56:33en
sync.item.modts2025.04.03 14:32:04en
thesis.grantorVysoké učení technické v Brně. Fakulta podnikatelská. Ústav financícs
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