Only 2 percent of Bitcoin node transactions were accounted to purchases on the dark web, according to the recent research from the blockchain analytics firm Elliptic with the help of the IBM Watson AI Lab from the Massachusetts Institute of Technology.
21 percent were identified as lawful, the vast majority of the transactions, roughly 77 percent, remained unclassified.
The research explored whether artificial intelligence could assist current anti-money laundering procedures. Researchers at the MIT-IBM Watson AI Lab used machine learning software to analyze 203,769 bitcoin node transactions worth roughly $6 billion in total.
The results of this study are in line with a research from competing analytics firm Chainalysis, which estimated just 1 percent of bitcoin transactions in 2019 were known to be associated with illicit activity.
As 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.