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

Stefánsson, Hilmar Páll
Grímsson, Huginn Sær
Þórðarson, Jón Kristinn
Oskarsdottir, Maria
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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.
Fraud Detection Using Machine Learning, bitcoin, blockchain, fraud detection, money laundering, unsupervised machine learning
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