While there is often much public discussion on the potential adverse impact of AI, related innovations offer the prospect of improving the detection and disruption of financial crime in the cryptoasset space. At Elliptic we are prioritizing research into how AI and machine learning can bolster blockchain analytics capabilities.

Blockchains provide fertile ground for machine learning techniques, thanks to the availability of both transaction data and information on the types of entities that are transacting, collected by us and others. This is in contrast to traditional finance where transaction data is typically siloed, making it challenging to apply these techniques.

Elliptic first published research on this topic in 2019, co-authored with researchers from the MIT-IBM Watson AI Lab. A machine learning model was trained to identify Bitcoin transactions made by illicit actors, such as ransomware groups or darknet marketplaces.

In May 2024, Elliptic released further research, applying new techniques to a much larger dataset, containing nearly 200 million transactions. This work is again co-authored by researchers from the MIT-IBM Watson AI Lab. Rather than identifying transactions made by illicit actors, a machine learning model was trained to identify “subgraphs”, chains of transactions that represent bitcoin being laundered. By identifying these subgraphs rather than illicit wallets, this approach allows us to focus on the “multi-hop” laundering process more generally rather than the on-chain behavior of specific illicit actors.

Elliptic worked with a cryptocurrency exchange to test whether this technique could be used to identify money laundering attempts through that business. Of 52 “money laundering” subgraphs predicted by the model and which ended with deposits to this exchange, the exchange confirmed that 14 had been received by users who had already been flagged as being linked to money laundering. On average less than one in 10,000 of these accounts are flagged as such, suggesting that the model performs very well. Importantly, the exchange’s insights were based on off-chain information, suggesting that the model can identify money laundering that would not be identifiable using traditional blockchain analytical techniques alone.

We also investigated the types of money laundering patterns that the trained model was identifying. This revealed known money laundering patterns such as “peeling chains”, which can already be automatically detected in Elliptic’s transaction and wallet screening tools. However, it also identified novel patterns such as the use of intermediary “nested services” in specific ways. Knowledge of these money laundering behaviors is of value to AML practitioners and investigators, and can be added to the suite of behaviors that can be detected with Elliptic’s tools.