Earlier this year, we announced a collaboration with the University of Birmingham to explore new approaches to Anti-Money Laundering (AML) in the context of emerging technologies. Since then, the focus has been on examining how academic research can be translated into work that can be evaluated in more realistic settings.
Anti-Money Laundering analysis for Blockchain Transactions remains a complex and evolving challenge. Existing systems often struggle to keep pace with new transaction patterns, regulatory expectations, and the growing need for transparency without compromising privacy. This collaboration looks at how research-driven methods may help navigate some of these tensions in practice.
In this post, we want to provide more context for this research, share broader context on AML best practices, and explain how our collaboration with the University of Birmingham fits into the direction we’ve set for the year ahead.
What is Anti-Money Laundering?
As the name suggests, AML is a set of frameworks and processes used to detect and prevent illicit financial activity, including money laundering, fraud, and other forms of financial crime. These measures exist to protect financial systems, support compliance, and reduce systemic risk.
In the context of crypto, AML often involves a combination of:
- Verifying customer identity (KYC)
- Monitoring suspicious transactions
- Analyzing on-chain activity
Transparency is one of blockchain’s defining characteristics. At the same time, making sense of on-chain data, particularly across multiple networks, introduces new analytical challenges. Understanding transaction flows, relationships between entities, and patterns of activity requires careful methods and clear assumptions. AI technology allows us to tackle these analytical challenges with renewed perspective and tools.
For us, working on AML is about contributing to this understanding. It’s not about replacing existing compliance systems, but about exploring how blockchain data can be analyzed in ways that support responsible use and regulatory clarity.
A step aligned with our longer-term goals
Supporting this type of research is also aligned with the goals we outlined in our 2026 outlook. One of those goals is to focus on work that strengthens the broader ecosystem around blockchain — not just at the protocol level, but in how these systems are understood, governed, and integrated into existing frameworks. Engaging with AML research is one part of that effort.
UoB x Nimiq Collaboration
This is where this collaboration starts. Academic research provides space to explore new methods, question existing assumptions, and test alternative approaches in a controlled environment. Translating this kind of research into something tangible is only right to understand what holds up outside theory.
Our collaboration with the University of Birmingham focuses on this transition. The aim is not to present a finished or production-ready solution, but to better understand how research-driven AML approaches might function in realistic blockchain contexts.
This work examines AML from a broad perspective: why regulation matters, how compliance requirements shape financial systems, and where innovation can improve effectiveness without compromising core principles. It is an exploratory effort, grounded in research and shaped by the need to balance regulation, usability, and technological change.
The Result
The collaboration has produced an early SynapTrack prototype that applies the research in a practical setting. It’s designed to explore how AML analysis can be performed on blockchain transaction data in a testable way. This milestone represents an opportunity to discuss the work, gather feedback, and better understand how research-driven AML approaches resonate beyond academic settings.
Want to collaborate or test SynapTrack in real-world conditions? Register your interest here.
Looking ahead
Partnering with the University of Birmingham has been a constructive and valuable experience. It reinforces the importance of collaboration between research and industry, especially when working on topics as complex and evolving as AML.