A new artificial intelligence (AI) model has been announced by blockchain analysis firm Elliptic in partnership with researchers from MIT and IBM which is meant to identify money laundering activities within Bitcoin transactions. To this effect, the announcement was made after the publishing of their research paper that outlines the methodology and efficiency of this new instrument.
Data Release to Aid in Crypto Crime Fighting
The researchers have also released a large dataset of 200 million Bitcoin transactions. This dataset, called Elliptic2, is designed to empower AI models in detecting illicit financial flows in the area of cryptocurrencies. It consists of transactional patterns from identified illicit actors to cryptocurrency exchanges where the said money could be laundered.
Tom Robinson, Elliptic’s chief scientist and co-founder, emphasized that this marks a paradigm shift in blockchain analytics. Rather than only finding transactions that are related to criminal activities, this new approach is based on detecting the patterns of transactions or “subgraphs” that imply potential money laundering.
Enhancing Detection Capabilities with AI
This AI model development marks an advanced step taken in the fight against criminal activities based on cryptocurrencies. In loading the AI with examples of transaction patterns directly associated with money laundering, Elliptic hopes to create a more effective tool that also reduces the number of false positives that often hinder the investigative processes.
In the testing phase, the model was implemented on a trial set of transaction data from a crypto exchange, and a number of suspicious transaction chains were detected. The internal systems of the exchange have already identified a large number of these, which shows the potential efficiency of the model.
Potential for Broader Impacts on AI Applications
Apart from its direct usage in crypto forensics, the extensive data and the methods created could also impact other realms of AI research, such as healthcare and recommendation systems. The data’s volume and granularity are a great information source for building many Machine Learning models to recognize complex patterns in different domains.
Although the new tool seems very promising, Stefan Savage, a computer science professor and an advisor to the research, noted that AI-based tools in forensic finance would raise new ethical and legal questions. The opaqueness of AI decision-making processes can raise the same issues as those associated with other sensitive AI applications like facial recognition technology.
Nevertheless, the release of the Elliptic dataset and the creation of the AI model are anticipated to greatly strengthen the fight against money laundering within the Bitcoin ecosystem.
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