Identifying Money Laundering on the Blockchain
InnovateUK CyberASAP funded project developing modern blockchain AML techniques with low false-positive rates.
Problem. Existing anti-money laundering (AML) tools for blockchain produce high false-positive rates, burdening compliance teams and missing sophisticated laundering patterns.
Approach. We develop graph-based and machine learning techniques to identify money laundering patterns on public blockchains, focusing on reducing false positives while maintaining high detection rates. We also study how sanctioned entities evade current compliance mechanisms.
Impact. Our techniques enable more effective blockchain compliance, helping financial institutions and regulators detect illicit activity without over-flagging legitimate users.
Funded by InnovateUK CyberASAP.