Now, they're not only releasing an experimental version of that AI model for detecting bitcoin money laundering but also publishing the training data set behind it: a 200-million transaction trove of Elliptic's tagged and classified blockchain data, which the researchers describe as the biggest of its kind ever to be made public by a thousandfold. “We're providing about a thousand times more data, and instead of labeling illicit wallets, we're labeling examples of money laundering which might be made up of chains of transactions,” says Tom Robinson, Elliptic's chief scientist and cofounder. “It's a paradigm shift in the way that blockchain analytics is used.”
Blockchain analysts have used machine learning tools for years to automate and sharpen their tools for tracing crypto funds and identifying criminal actors. In 2019, in fact, Elliptic already partnered with MIT and IBM to create a AI model for detecting suspicious money movements and released a much smaller data set of around 200,000 transactions that they had used to train it.
WIRED
A Vast New Data Set Could Supercharge the AI Hunt for Crypto Money Laundering
Blockchain analysis firm Elliptic, MIT, and IBM have released a new AI model—and the 200-million-transaction dataset it's trained on—that aims ...
