SHA256X2 on Nostr: Blockchain analysts have used machine learning tools for years to automate and ...
Blockchain analysts have used machine learning tools for years to automate and sharpen their tools for tracing crypto funds and identifying criminal actors. In 2019, in fact, Elliptic already partnered with MIT and IBM to create a AI model for detecting suspicious money movements and released a much smaller data set of around 200,000 transactions that they had used to train it.
For this new research, by contrast, the same team of researchers took a much more ambitious approach. Rather than try to classify single transactions as legitimate or illicit, Elliptic analyzed collections of up to six transactions between Bitcoin address clusters it had already identified as illicit actors and the exchanges where those previously identified shady entities sold their crypto, positing that the patterns of transactions between criminals and their cashout points could serve as examples of money laundering behavior.
Working from that hypothesis, Elliptic assembled 122,000 of these so-called subgraphs, or patterns of known money laundering within a total data set of 200 million transactions. The research team then used that training data to create an AI model designed to recognize money laundering patterns across Bitcoin's entire blockchain.
https://www.wired.com/story/ai-crypto-tracing-model-money-laundering/
For this new research, by contrast, the same team of researchers took a much more ambitious approach. Rather than try to classify single transactions as legitimate or illicit, Elliptic analyzed collections of up to six transactions between Bitcoin address clusters it had already identified as illicit actors and the exchanges where those previously identified shady entities sold their crypto, positing that the patterns of transactions between criminals and their cashout points could serve as examples of money laundering behavior.
Working from that hypothesis, Elliptic assembled 122,000 of these so-called subgraphs, or patterns of known money laundering within a total data set of 200 million transactions. The research team then used that training data to create an AI model designed to recognize money laundering patterns across Bitcoin's entire blockchain.
https://www.wired.com/story/ai-crypto-tracing-model-money-laundering/