New System Tracks Blockchain Money Laundering Faster and More Accurately

New system provides a next-generation anti-money laundering framework for blockchain systems, with automatic adaptation to new criminal tactics.

An advanced detection system to identify and trace blockchain funds connected with criminal activity has been presented at the Annual CyberASAP Demo Day in London.

The system, called SynapTrack, enables faster and more accurate detection of fraudulent activity using blockchains and cryptocurrencies, where traditional anti-money laundering and counter-terrorist financing systems struggle to keep pace.

Although current fraud detection methods pick up unusual activity, they deliver an extremely high rate (40%) of false positive reports. These require manual checking by compliance professionals, resulting in backlogs in identifying and acting on suspicious activity.

The SynapTrack system is designed to deliver a substantially lower rate of false positives. It has already been tested using real-life data from the notorious 2025 Bybit hack, where criminals stole $1.5bn of digital tokens from a cryptocurrency exchange. SynapTrack traced the hacker with 98% accuracy.

The team behind SynapTrack is keen to hear from exchanges, financial regulators or law enforcement agencies who want to test the prototype in real-world conditions.

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SynapTrack uses a validated methodology to score the likelihood of transactions being part of a money laundering scheme. It has a self-improving algorithm that continuously adapts to new tactics – dynamically identifying suspicious patterns in blockchain transactions. It has a universal cross-chain capability, and is designed around how compliance teams work, presenting results in a dashboard. No infrastructure changes are needed for installation.

It is relatively easy to obscure fraudulent or criminal activity by moving funds between blockchains, or dispersing them across many blockchains, in what are known as ‘cross-chain’ transactions. It is these transactions that pose the greatest difficulty for existing anti-money laundering systems.

SynapTrack was developed by University of Birmingham computer scientists Dr Pascal Berrang and PhD student Endong Liu, in collaboration with blockchain developer Nimiq. Dr Berrang’s research is in IT security and privacy on blockchain, artificial intelligence and machine learning. The subject of Endong Liu’s PhD is transaction tracing. Nimiq is supporting with blockchain-specific insights, knowledge of real-world constraints, and implementation.

The team is currently fundraising to ensure regulatory readiness and complete the team with a CEO and software developers.

Dr Berrang said: “The last few years have seen a near-exponential growth in blockchain transactions. While many of these are legitimate, blockchains are attractive to criminals as funds can be moved very quickly to other jurisdictions. Our work with Nimiq and the creation of SynapTrack is addressing this black spot, and will enable more effective regulation, making the whole ecosystem of blockchain safer and more trustworthy.”

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