Criminals thrive on inconsistency. In the world of anti-money laundering (AML), gaps in jurisdictional oversight, fragmented reporting frameworks, and variable regulatory standards create opportunities that are routinely exploited.
According to Conisilient, even within regulated institutions, weak internal controls or limited visibility across borders allow illicit funds to slip through unnoticed. Transactions flagged in one country can easily go unchecked in another, while discrepancies in ownership disclosure and information-sharing protocols form blind spots that criminals rely on as entry points—not as fallback options.
Regulatory bodies are now moving to close these gaps. In recent years, collaboration between financial intelligence units (FIUs), supranational authorities, and national regulators has begun to take shape. Tools such as FATF evaluations, bilateral memoranda of understanding (MoUs), and the forthcoming EU Anti-Money Laundering Authority (AMLA) point to a more coordinated system of oversight. But aligning policy is only part of the solution. What’s needed is a secure and scalable infrastructure for sharing intelligence—without compromising local control of sensitive data.
Fragmentation remains the biggest weakness in the current system. Cross-border financial crime exploits variations in enforcement, disclosure thresholds, and reporting requirements. Criminal networks plan their activities around these weak spots. Funds are funnelled through offshore entities, high-risk payment service providers (PSPs), crypto platforms, and nested correspondent accounts. Without real-time, shared insight into these flows, AML defences fail to capture the full picture.
But there is progress. The EU’s AMLA will begin direct supervision of high-risk financial institutions across the bloc by 2026, enforcing consistent standards across member states. The Egmont Group, now connecting over 160 FIUs globally, is stepping up its operational role in coordinating cases and sharing typologies. Bilateral MoUs between G7 nations are also accelerating intelligence sharing on crypto risks, sanctions violations, and offshore transactions. Meanwhile, capacity-building initiatives from FATF and the UNODC are strengthening the AML infrastructure in lower-capacity jurisdictions.
While these measures are promising, practical collaboration still faces significant obstacles. Many financial institutions and regulators operate on outdated, closed systems that cannot exchange data securely. Even when there is willingness to collaborate, legal and reputational concerns around data sharing frequently derail efforts. Asymmetries in regulatory maturity create further vulnerabilities, while inconsistent expectations around AI model governance erode trust in shared outputs.
Even with the political will in place, real change is hard. Data privacy laws, strained resources, and conservative attitudes towards change all contribute to the slow pace of progress. In some cases, institutions are simply too overwhelmed by day-to-day compliance demands to invest in building scalable solutions. Legacy technology, still widely in use, makes the process even more cumbersome.
Despite these barriers, real-world examples offer hope. The Netherlands’ Transaction Monitoring Netherlands (TMNL) project has demonstrated what’s possible when banks collaborate. The Egmont Group could evolve into a central hub for global AML coordination—if supported with the right mandate and tools.
What AML needs now is not just alignment, but infrastructure designed for collaborative intelligence. This is where Federated Learning comes in. Unlike traditional data-sharing models, Federated Learning enables institutions and regulators to train AI models across separate environments without moving any data. Each participant retains full control of their data, while benefiting from shared learning at scale.
The impact is tangible. One major US bank using Federated Learning saw a 75% improvement in efficiency and identified four times as many relevant risks compared to its existing system. The approach allowed the institution to detect threats that its siloed infrastructure had missed.
Consilient is at the forefront of this shift, offering pre-trained, explainable models that operate across secure, regulated environments. These models empower financial institutions to improve detection and compliance while preserving privacy. Supervisors gain deeper insights without added complexity. It’s a practical, proven solution to a longstanding global challenge.
As AML regulation continues to evolve, the path to meaningful cooperation will depend on more than policy. It will require systems built to collaborate—securely, flexibly, and without compromise.
Copyright © 2025 RegTech Analyst
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