Smaller financial institutions are facing mounting structural challenges in meeting anti-money laundering (AML) requirements, despite being held to the same regulatory standards as their larger peers.
According to Consilient, regulators, including FinCEN in the US and the UK’s FCA, have acknowledged the resource constraints these institutions face, such as limited access to advanced compliance technology and difficulties attracting experienced compliance professionals.
From community banks and credit unions to MSBs, crypto platforms, and neobanks, smaller firms must navigate complex AML obligations without the infrastructure or economies of scale that benefit larger players. The result is growing operational strain and a widening compliance gap, even as regulatory scrutiny intensifies across the board.
This disparity has become more pronounced as financial criminals target smaller institutions for their perceived weaknesses, while regulators move to enforce consistent standards across all firm sizes. FinCEN, the Federal Reserve, and the OCC are among those signalling that no institution—regardless of size—will be exempt from meeting baseline AML effectiveness.
In October 2024, for example, the OCC issued a Cease and Desist Order against Clear Fork Bank, a small community bank, for AML failures, highlighting the shift in supervisory focus. Smaller firms are now expected to demonstrate effective compliance programmes despite lacking many of the tools and resources available to larger financial institutions.
Among the most pressing challenges are the sustainability of compliance costs, limited access to top-tier AML technology, and thin staffing across compliance and risk functions. Many smaller firms depend on outdated systems or manual processes, leaving them more vulnerable to operational failures and regulatory breaches.
In addition, the sector faces a skills gap. Recruiting experienced compliance professionals is a significant challenge, especially for firms operating in rural or early-stage environments. This often leads to incomplete risk assessments, weak customer due diligence, and insufficient alert triage.
These vulnerabilities are not just internal issues—they represent systemic risk. Underdeveloped onboarding, ineffective transaction monitoring, and poor governance at the periphery of the financial system can allow illicit activity to flow undetected into the broader financial ecosystem.
Once inside, criminal funds can move through less monitored transaction channels, ultimately landing in larger institutions that rely on upstream firms to maintain adequate controls. This exposure is prompting larger financial players to reassess their risk models and collaborate more actively across their networks.
Federated Learning, a machine learning approach that enables collaborative risk model development without sharing data, is emerging as a potential game-changer. Companies like Consilient are helping financial institutions pool intelligence securely, giving smaller firms access to high-performance AML models that have been trained across multiple institutions.
According to Consilient, their Federated Learning AML models have demonstrated up to 4x higher detection effectiveness and 75% efficiency gains without compromising sensitive data. This approach allows even the smallest players in the sector to contribute to and benefit from shared intelligence, boosting system-wide defences.
Ultimately, strengthening AML performance at every level of the financial system is critical. Regulatory tolerance for weak controls is evaporating, and the industry must evolve to meet this challenge collectively. Technology, collaboration, and regulatory alignment will be essential in closing the compliance gap and safeguarding the financial ecosystem.
Copyright © 2025 FinTech Global
