Elliptic Partners With MIT Researchers To Monitor Illicit Activities In A Transaction

Elliptic Partners With MIT Researchers To Monitor Illicit Activities In A Transaction

Blockchain News
August 3, 2019 Editor's Desk
3038
Bitcoin’s sodality with fraudulent activities like hacks and drug trafficking is pointed out often by the critics, but detecting these activities in Bitcoin Is comparatively easier compares than physical money because of its anonymous and transparent property. Elliptic, a blockchain analytics start-up has now integrated with the tech giant IBM and researchers from MIT (Massachusetts
Elliptic, MIT

Bitcoin’s sodality with fraudulent activities like hacks and drug trafficking is pointed out often by the critics, but detecting these activities in Bitcoin Is comparatively easier compares than physical money because of its anonymous and transparent property.

Elliptic, a blockchain analytics start-up has now integrated with the tech giant IBM and researchers from MIT (Massachusetts Institute of Technology), in order to implement deep learning techniques to verify over 2,00,000 transactions of bitcoin as part of an effort to determine activities like ransomware and money laundering.

Elliptic has released a data ser, according to the analytics, claiming to “the world’s largest set of labeled transaction data publicly available for any cryptocurrency.”The data set has been made with the help of various machine learning methods like Random Forest, Logistic Regression, Multilayer Perceptions and Graph Convolutional Networks (GCN), with GCN being a new emerging method.

“Graph convolutional networks are still a young class of methods, and we’re early days in these experiments, but we do believe GCN’s power to capture the relational information in these large, complex transaction networks could prove valuable for anti-money laundering,” said researcher at MIT-IBM Watson AI Lab, Mark Weber, who took part in the analysis of bitcoin transactions.

Elliptic’s data set comprises of a time-series graph of about 203,769 bitcoin transactions and payment flows. The analytics show that 2% of the analyzed transactions were unlawful, 21% were licit, and the rest were marked as unknown.

Even though the current focus is on bitcoin (BTC), it could be used in several other cryptocurrencies like Ethereum (ETH) to Facebook’s upcoming crypto, Libra.

The MIT and IBM researchers say that they hope to inspire others to adopt such techniques that are emerging to combat “societally important challenge” of determining money laundering activities and ultimately making financial systems “safer and more inclusive.”

A blockchain analytics firm, CipherTrace, recently said that “sophisticated” technology is required to kick out money laundering activities in cryptocurrencies. “Tracking funds through the Blockchain system requires the most advanced computer science and cybercrime knowhow,” it said. Another blockchain analytics firm called Chainalysisalso helped financial institutions, exchanges, and law enforcement agencies to detect such unlawful activities across more blockchains’ networks earlier in 2019.

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