SynapTrack is a detection system developed by the University of Birmingham in collaboration with blockchain developer Nimiq, designed to identify illicit activity across blockchains more quickly and accurately than traditional AML tooling. The system uses a validated methodology to score the likelihood that a transaction is part of money laundering, and it is built around a dashboard that aligns with how compliance teams operate. A key feature is its self-improving algorithm that continuously adapts to new tactics as criminals evolve, enabling dynamic identification of suspicious blockchain patterns across multiple chains.
The technology was demonstrated with real-world data from the 2025 Bybit hack, in which criminals stole approximately $1.5 billion in digital tokens. In that test, SynapTrack traced the hacker with 98% accuracy, highlighting a significant improvement over many existing fraud-detection methods that yield high false-positive rates. The Birmingham-Nimiq team emphasizes that SynapTrack can deliver a substantially lower rate of false positives, reducing the manual load on compliance professionals.
SynapTrack’s design emphasizes cross-chain capability and regulator-ready workflows, providing results in a user-friendly dashboard that fits the speed and accuracy needs of exchanges, regulators, and law enforcement. The project is being developed by Birmingham researchers Dr. Pascal Berrang and PhD student Endong Liu in partnership with Nimiq, aiming to address the challenges of cross-chain activity and evolving laundering techniques. The team is actively seeking exchanges and regulators to test the system in real-world conditions, hoping to enable more effective regulation and safer, more trustworthy blockchain ecosystems.
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