MIT's AI Lab Crunched 200,000 Bitcoin Transactions. Only 2% Were 'Illicit'

Blockchain analytics firm Elliptic collaborated with researchers from the Massachusetts Institute of Technology to publish a public dataset of bitcoin transactions associated with illicit activity.

The group's study detailed how researchers at the MIT-IBM Watson AI Lab used machine learning software to categorize 203,769 bitcoin node transactions worth roughly $6 billion in total.

The research explored whether artificial intelligence could assist current anti-money laundering procedures.

After examining the nodes' association with known entities, researchers found only 2 percent of those 200,000 bitcoin transactions were deemed illicit.

While 21 percent were identified as lawful, the vast majority of the transactions, roughly 77 percent, remained unclassified.

The 2 percent figure in line with a study from competing analytics firm Chainalysis, which estimated just 1 percent of bitcoin transactions in 2019 were known to be associated with illicit activity.

Since Elliptic is frequently hired by law enforcement agencies around the world to identify illegal activities using cryptocurrency, this research aimed to identify patterns that can help distinguish illicit usage from lawful bitcoin usage, especially among unbanked individuals or other unknown entities.

"A big problem with compliance, in general, is false positives. A big part of this research is minimizing the number of false positives," Elliptic co-founder Tom Robinson told CoinDesk.

"The key finding is that machine learning techniques are very effective at finding transactions that are illicit."

A 2017 report by the American Institute for Economic Research, estimated that "More than a third of all US currency in circulation is used by criminals and tax cheats."