Bitcoin
Bitcoin Forensic Analysis Reveals Money Laundering Rings and Criminal Proceeds
May 1, 2024WritingFinancial Crime/Forensic Analysis
A forensic analysis of a graph dataset containing transactions on the Bitcoin blockchain has revealed clusters associated with illicit activity and money laundering, including the detection of criminal proceeds sent to a cryptocurrency exchange and previously unknown wallets belonging to a Russian darknet marketplace.
O discoveries come from Elliptic in collaboration with researchers at the MIT-IBM Watson AI Lab.
The 26GB dataset, dubbed Elliptical2is a “large graph dataset containing 122 thousand labeled subgraphs of Bitcoin clusters in a background graph consisting of 49 million node clusters and 196 million edge transactions,” the co-authors he said in an article shared with The Hacker News.
Elliptic2 is based on Elliptical Dataset (also known as Elliptic1), a trading chart that was released in July 2019 with the aim of combating financial crime using graphical convolutional neural networks (GCNs).
The idea, in a nutshell, is to uncover illegal activities and money laundering patterns, taking advantage of blockchain pseudonymity and combine it with knowledge about the presence of licit (e.g. exchange, wallet provider, miner, etc.) and illicit (e.g. darknet market, malware, terrorist organizations, Ponzi scheme, etc.) services on the network .
“Using machine learning at the subgraph level – that is, the groups of transactions that constitute money laundering cases – can be effective in predicting whether crypto transactions constitute proceeds of crime,” said Tom Robinson, chief scientist and co-founder of Elliptic. Hacker news.
“This is different from conventional combating crypto money laundering (LBC), which rely on tracking funds from known illicit wallets or pattern matching with known money laundering practices.
The study, which experimented with three different subgraph classification methods in Elliptic2, such as GNN-Seg, Sub2VecIt is GLASSidentified subgraphs that represented cryptocurrency exchange accounts potentially involved in illegitimate activities.
Additionally, it made it possible to trace the origin of funds associated with suspicious subgraphs to several entities, including a cryptocurrency mixer, a Panama-based Ponzi scheme, and an invite-only Russian dark web forum.
Robinson said that just considering the “shape” – the local structures within a complex network – of money laundering subgraphs has proven to be an already effective way of flagging criminal activity.
Further examination of the predicted subgraphs using the trained GLASS model also identified known cryptocurrency laundering patterns, such as the presence of peeling chains and nested services.
“A peel chain is where a small amount of cryptocurrency is ‘peeled’ to a destination address, while the remainder is sent to another address under the user’s control,” Robinson explained. “This happens repeatedly to form a peeling chain. The pattern may have legitimate financial privacy purposes, but it may also be indicative of money laundering, especially when the ‘peeled’ cryptocurrency is repeatedly sent to an exchange.”
“This is a known crypto laundering technique and has an analogy to ‘smurfing’ in traditional finance – so the fact that our machine learning mode has independently identified it is encouraging.”
As for next steps, research is expected to focus on increasing the accuracy and precision of these techniques, as well as extending the work to other blockchains, Robinson added.
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