Elliptic announces the release of the Elliptic Data Set the world’s largest set of labeled transaction data publicly available in any cryptocurrency — to motivate and enable the development of new techniques for detection of illicit cryptocurrency transactions.
This release coincides with a new paper that Elliptic scientists have co-authored with researchers from the MIT-IBM Watson AI Lab. The paper, entitled, “Anti-Money Laundering in Bitcoin: Experiments with Graph Convolutional Networks for Financial Forensics,” will be presented by IBM Research Staff Members Mark Weber and Giacomo Domeniconi at the Anomaly Detection in Finance workshop of the Knowledge Discovery and Data Mining Conference (KDD) on August 5, 2019.
Money laundering is the process of obfuscating money transfers originating from criminal activity. Billions of dollars of criminal proceeds are laundered through cryptocurrencies each year. Recent advancements in deep learning for graph or network structured data show promise for identifying bad actors in complex money laundering schemes. Through this scientific work, Elliptic aims to help its clients to more effectively and efficiently identify illicit transactions, reducing compliance costs and driving criminal activity out of cryptocurrencies.
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The Elliptic Data Set consists of 200,000 bitcoin transactions with a total value of $6 billion. Transactions identified by Elliptic research as having been made by criminal actors have been labeled, to allow the development and testing of new predictive techniques.
Elliptic is the leading provider of blockchain monitoring solutions for regulatory compliance and risk management by cryptocurrency businesses and financial institutions worldwide. Machine learning is already used within Elliptic’s products to identify cryptocurrency transactions associated with money laundering, sanctions violation or terrorist financing. These insights are used by clients including cryptocurrency exchanges and financial institutions to meet their compliance obligations.
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According to the paper’s co-author Mark Weber: “Graph convolutional networks are still a young class of methods, and we’re in the 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.”
Chief Scientist and co-founder of Elliptic, Tom Robinson said, “Elliptic uses a range of advanced techniques, including machine learning, to facilitate financial crime detection in cryptocurrencies. Our work with researchers from the MIT-IBM Watson AI Lab builds on this, to ensure that our clients have access to the most accurate and effective insights available, reducing their compliance costs and ensuring that their services are not exploited by criminals.”