MIT’s AI Lab Analyzed 200,000 Bitcoin Transactions. Only 2% Were ‘Illicit’

Blockchain analytics firm Elliptic collaborated with researchers from the Massachusetts Institute of Technology (MIT) 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 (AML) procedures.

Only 2 percent of the 200,000 bitcoin transactions in the data set were deemed illicit. While 21 percent were identified as lawful, the vast majority of the transactions, roughly 77 percent, remained unclassified. (To date, there have been an estimated 440 million bitcoin transactions since the network’s launch in 2009.)

The 2 percent figure in line with a study from competing analytics firm Chainalysis, which estimated just 1 percent of bitcoin transactions in 2019…