The recent research, conducted jointly by Blockchain analytics company Elliptic, researchers from Massachusetts Institute of Technology (MIT) and tech giant IBM, showed an overwhelming bitcoin transaction clearness.

The aforesaid partnership was organized in order to use deep learning developments and analyze bitcoin transactions in the context of measures to detect illicit activities, such as money laundering. The group’s study sets out in details how scientists at the MIT-IBM Watson AI Lab employed machine learning software to analyze 203,769 bitcoin node transactions, with a total value of approximately $6 billion.

According to the research, only 2 percent of the analyzed transactions were deemed illicit. Meanwhile, 21 percent were determined as legal, whereas the remaining 77 percent of transactions remain not classified. Until now, there have been an estimated 440 million bitcoin transactions following the network’s launch in 2009.

While Elliptic is regularly hired by law enforcement agencies throughout the world with regards to cryptocurrency transactions identification, the purpose of this research was to determine main patterns that might be useful in distinguishing illicit usage from lawful bitcoin utilization.

Elliptic co-founder Tom Robinson said,

"A big problem with compliance, in general, is false positives. A big part of this research is minimizing the number of false positives. The key finding is that machine learning techniques are very effective at finding transactions that are illicit. Sometimes, the software was able to find patterns that would be difficult to describe yet still matched with known entities, based on pre-existing data from darknet markets, ransomware attacks and other criminal investigations."

The above-mentioned company made the same dataset public with the purpose of encouragement of open-source developments.

MIT expert Mark Weber also said that the results of initial experiments were published as means to solicit feedback from crypto experts and help to inspire others to join the effort to make financial systems safer by developing new techniques and models for AML.

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