Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/79526

Detecting potential money laundering addresses in the Bitcoin blockchain using unsupervised machine learning

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Title:Detecting potential money laundering addresses in the Bitcoin blockchain using unsupervised machine learning
Authors:Stefánsson, Hilmar Páll
Grímsson, Huginn Sær
Þórðarson, Jón Kristinn
Oskarsdottir, Maria
Keywords:Fraud Detection Using Machine Learning
bitcoin
blockchain
fraud detection
money laundering
show 1 moreunsupervised machine learning
show less
Date Issued:04 Jan 2022
Abstract:Money laundering is a serious problem worldwide, especially in the crypto market. This is mostly because of the anonymity that many cryptocurrencies offer. That is one of the reasons why cryptocurrencies are a haven for money laundering, because it is easier for criminal entities to buy the currency and then trade it for real fiat money. Detecting money laundering in cryptocurrency can be tricky because the crypto network is large and convoluted and nearly impossible to analyze by hand. What we can do is look at addresses that took part in transactions as actors and then use machine learning to predict what addresses are possibly laundering money. In this paper we intend to analyze methods that can be used to detect money laundering in Bitcoin using machine learning to empower investigators to more accurately and efficiently determine whether a suspicious activity is money laundering.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/79526
ISBN:978-0-9981331-5-7
DOI:10.24251/HICSS.2022.194
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Fraud Detection Using Machine Learning


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