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 ... (full story)

In my work at the MIT-IBM Watson AI Lab, I am collaborating with a special group of people inspired to harness the powers of deep learning and high performance computing to fight money laundering. Anti-money laundering (AML) is a complex problem and we don’t have delusions of being superheroes who save the day, but we believe AI can play a powerful role and we’re here to do our part as researchers. We’re especially excited about the potential of Graph Convolutional Networks (GCN), an emergent class of methods capable of capturing relational information, which is important given complex layering and obfuscation ... (full story)