Blockchain Sleuth Elliptic Explores AI and Anti-Money Laundering Using 200M Bitcoin Transactions

In 2019, we conducted a study utilizing a transaction dataset comprising 200,000 records. Our latest project builds upon that research, employing the considerably larger “Elliptic2” dataset. This new dataset consists of over 122,000 identified “subgraphs,” which are clusters of interconnected nodes and chains of transactions previously flagged for their involvement in illicit activities.

As an analyst, I’ve discovered that several questionable subgraphs consist of what are infamously called “peeling chains.” In these scenarios, a user transfers cryptocurrency to a specific destination address, but keeps the change or remainder for themselves. This process is repeated multiple times to create an intricate chain.

“According to Ellptic’s research paper, in conventional finance, large sums of money are broken down into numerous smaller transactions to bypass regulatory thresholds and evade scrutiny. This practice is referred to as ‘smurfing.'”

As a crypto investor, I’ve come across the practice of using “intermediary services” or “nested platforms” in my transactions. These are businesses that facilitate fund transfers between accounts on larger cryptocurrency exchanges, often without the explicit consent of the exchange itself.

As a financial analyst, I’ve discovered that nested services, which are integrated into larger platforms, often have less rigorous customer due diligence procedures than the cryptocurrency exchanges they rely on. In some cases, these nested services don’t even conduct any anti-money laundering checks at all. This lack of scrutiny makes them attractive targets for criminals looking to launder cryptocurrencies, which may lead to their inclusion in suspicious subgraphs according to the model I’ve been analyzing.