The collaboration is building a tool to score the likelihood of a transaction being part of a money laundering scheme, using an algorithm trained on data from known money laundering cases such as the 2025 $1.5bn Bybit Hack.
The initiative is being led by researchers from Birmingham’s School of Computer Science, and will combine academic research and real-world blockchain experience, with Nimiq developers supporting the project with domain expertise and technical contributions, and helping translate research ideas into practical, testable concepts.
Blockchain usage increasingly spans multiple chains, bridges, and DeFi systems. Existing fraud detection methods struggle to monitor this cross-chain activity, and produce a lot of false positives that require manual cross-checking by investigators and compliance teams.
The research element of the collaboration will look at:
- how patterns of activity indicating potential money laundering can be detected across multiple blockchains, when money is moved between different chains and systems.
- investigator-facing workflows, focused on search, context, and explainability
- applied proof-of-concept work, designed to demonstrate end-to-end analysis in a realistic setting
Nimiq’s role is to support the research and add value with blockchain-specific insights, knowledge of real-world constraints, and targeted implementation.
The project aligns with Nimiq’s broader goals of making blockchain systems easier to understand, integrate, and operate responsibly, while maintaining usability and as much privacy as possible.
We’ll be posting updates and insights to the community as work progresses.