Correctly identifying 14 out of 52 of those customer accounts as suspicious may not sound like a high success rate, but the researchers point out that only 0.1 percent of the exchange's accounts are flagged as potential money laundering overall.
Their automated tool, they argue, had essentially reduced the hunt for suspicious accounts to more than one in four. 'Going from 'one in a thousand things we look at are going to be illicit' to 14 out of 52 is a crazy change,' says Mark Weber, one of the paper's co-authors and a fellow at MIT's Media Lab.
Analyzing the source of funds for some suspicious transaction chains identified by the model helped them discover Bitcoin addresses controlled by a Russian dark web market, a cryptocurrency 'mixer' designed to obfuscate the trail of bitcoins on the blockchain, and a Panama-based Ponzi scheme.
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