Blockchain analytics startup Elliptic and researchers at MIT and IBM to publish a public dataset of bitcoin transactions associated with illicit activity. Using machine learning software, the group analyzed over 200,000 bitcoin transactions, worth roughly $6 billion in total, to detect illicit activities such as money laundering and ransomware.

Elliptic’s released data set, dubbed as “the world’s largest set of labeled transaction data publicly available for any cryptocurrency,” was formed using  several machine learning methods such as Logistic Regression, Random Forest, Multilayer Perceptrons, and Graph Convolutional Networks (GCN), with the GCN being an “emergent new method.”

“Graph convolutional networks are still a young class of methods, and we’re early days in these experiments, but we do believe GCN’s power to capture the relational information in these large, complex transaction networks could prove valuable for anti-money laundering,” said Mark Weber, a researcher at MIT-IBM Watson AI Lab.

Out of the 203,769 bitcoin transactions that were analyzed, only 2%  were deemed illicit. 21% were identified as lawful, while the vast majority of the transactions, roughly 77 percent, remained unclassified.

“This work will contribute to enabling our clients, including cryptocurrency exchanges and financial institutions, to use our software to better identify illicit transactions and meet their anti-money laundering obligations,” Dr. Tom Robinson, chief scientist and co-founder of Elliptic, told The Block.

Elliptic is frequently hired by law enforcement agencies throughout the world to identify illegal activities using cryptocurrency, so this new research was aimed to identify patterns that can help distinguish illicit usage from lawful bitcoin usage. “A big problem with compliance, in general, is false positives. A big part of this research is minimizing the number of false positives,” Robinson told CoinDesk. “The key finding is that machine learning techniques are very effective at finding transactions that are illicit.”

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