By Andy Greenberg

One task where AI tools have proven to be particularly superhuman is analyzing vast troves of data to find patterns that humans can't see, or automating and accelerating the discovery of those we can. That makes Bitcoin's blockchain, a public record of nearly a billion transactions between pseudonymous addresses, the perfect sort of puzzle for AI to solve. Now, a new study—along with a vast, newly released trove of crypto crime training data—may be about to trigger a leap forward in automated tools' ability to suss out illicit money flows across the Bitcoin economy.

On Wednesday, researchers from cryptocurrency tracing firm Elliptic, MIT, and IBM published a paper that lays out a new approach to finding money laundering on Bitcoin's blockchain. Rather than try to identify cryptocurrency wallets or clusters of addresses associated with criminal entities such as dark-web black markets, thieves, or scammers, the researchers collected patterns of bitcoin transactions that led from one of those known bad actors to a cryptocurrency exchange where dirty crypto might be cashed out. They then used those example patterns to train an AI model capable of spotting similar money movements—what they describe as a kind of detector capable of spotting the “shape” of suspected money laundering behavior on the blockchain.

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.”