The U.S. sanctions regime remains the most expansive and enforcement driven in the global financial system. The Office of Foreign Assets Control (OFAC) continues to demonstrate a willingness to pursue civil penalties across sectors and jurisdictions, including cases involving indirect facilitation, shipping practices, correspondent banking exposure, and sanctions evasion through complex intermediary structures.

In parallel, U.S. authorities have repeatedly emphasised the importance of risk-based compliance programmes capable of adapting to evolving geopolitical and typological developments. Public enforcement notices and compliance guidance stress the need for institutions to identify red flags associated with evasion, not merely to perform mechanical list screening.

In this context, behaviour based sanctions surveillance is not a discretionary enhancement. It is increasingly a defensive necessity.

The Structural and Extraterritorial Character of U.S. Sanctions

U.S. sanctions are distinguished by three features: breadth, speed, and extraterritorial reach. Sectoral measures, secondary sanctions frameworks, export controls, and price cap enforcement mechanisms create exposure that extends beyond direct dealings with designated persons. Institutions may incur risk through facilitation of prohibited conduct, even where underlying counterparties are not themselves listed.

Secondary sanctions provisions heighten this exposure. Non-U.S. institutions may face consequences for supporting sanctioned sectors or actors, regardless of domicile. As a result, U.S. sanctions compliance has global implications.

The structural nature of recent regimes further complicates detection. Restrictions tied to energy markets, shipping services, advanced technologies, and financial facilitation require institutions to assess the substance of transactions, not only the identity of counterparties.

Traditional screening frameworks, while essential, do not fully address these dynamics.

Evasion Typologies in the U.S. Enforcement Landscape

U.S. enforcement actions and public advisories have highlighted recurring evasion techniques, including:

• Use of front companies and proxy buyers to acquire restricted goods

• Manipulation of shipping documentation and vessel identification practices

• Complex ownership structures designed to obscure control

• Re-routing of goods through intermediary jurisdictions

• Fragmented payments across correspondent chains to dilute transparency

These typologies often involve behaviour that appears commercially plausible when viewed in isolation. Detection requires the ability to connect patterns across trade, payments, customer networks, and geographic movement.

The question is not whether institutions can identify designated names. It is whether they can detect the architecture of evasion.

As James Booth, Global Head of AML, CTF and Sanctions at Silent Eight, notes:

In the U.S., enforcement has made clear that institutions are expected to understand how sanctions are circumvented, not just who is listed. Pattern recognition is no longer optional.

Behaviour Based Surveillance in Practice

Behaviour based sanctions controls seek to identify elevated risk through correlation of indicators aligned to known typologies. In the context of the U.S., these may include:

• Sudden shifts in trade flows to or from jurisdictions frequently cited in enforcement advisories

• Concentration of high value transactions in sectors subject to sectoral restrictions

• Repeated use of newly-incorporated entities in sensitive commodity transactions

• Network connections between customers and previously sanctioned actors through shared directors or addresses

• Payment patterns that consistently route through higher risk correspondent institutions without economic justification

Such indicators are rarely conclusive on their own. Their significance emerges when assessed collectively against a defined risk hypothesis derived from regulatory guidance and enforcement precedent.

Artificial Intelligence and Pattern Detection

Given the scale and complexity of U.S. related financial flows, AI plays an increasingly central role in behaviour-based sanctions detection. Machine learning models can analyse large volumes of transactional and network data to identify anomalies that may not be visible through static rule sets.

Applications include:

• Graph analytics to uncover indirect ownership and control relationships

• Anomaly detection models that identify unusual shifts in corridor exposure

• Entity resolution techniques to link fragmented identities across datasets

• Predictive prioritisation models to focus investigative resources on higher risk behavioural patterns

However, the use of AI in U.S. sanctions compliance must be aligned with regulatory expectations regarding model governance and transparency. Supervisory authorities expect institutions to understand and explain their control frameworks. AI models that produce opaque or non-interpretable outputs may create additional risk.

Robust governance therefore requires:

• Clear documentation of model objectives and design

• Validation processes assessing accuracy, stability and limitations

• Defined escalation criteria linked to regulatory risk

• Ongoing monitoring for model drift

• Human oversight with documented override processes

AI should enhance the institution’s ability to evidence a thoughtful compliance programme, not replace judgement or accountability.

Dynamic Corridor and Sector Risk

U.S. sanctions exposure often concentrates in specific trade corridors and sectors, including energy, maritime transport, and advanced technology supply chains. Institutions should therefore adopt dynamic corridor risk frameworks that reflect both enforcement trends and geopolitical developments.

Metrics defining a sanctions sensitive corridor in the U.S. context may include:

• Volume of activity in sectors subject to active enforcement focus

• Historical linkage to advisories issued by U.S. authorities

• Concentration of flows through intermediary jurisdictions associated with rerouting

• Patterns of vessel or shipping behaviour aligned to previously identified typologies

Recalibration should be event-driven as well as periodic. Major designation waves, new executive orders or updated guidance may materially alter exposure within short timeframes.

Early Warning and Proactive Mitigation

U.S. authorities have emphasised the value of proactive compliance measures. Institutions that identify and address potential sanctions risk before enforcement intervention are better positioned to demonstrate programme effectiveness.

AI-enabled behavioural surveillance supports early warning capability. By detecting emerging anomalies in trade or payment patterns, institutions can escalate concerns to governance forums before formal designation or public enforcement action.

Effective early warning systems rely on tiered escalation models. Behavioural signals inform enhanced due diligence or targeted review rather than automatically generating high volumes of standalone alerts. This approach balances sensitivity with operational sustainability.

Measuring Exposure Beyond Alert Counts

In the U.S. enforcement environment, credible reporting to boards and regulators requires more than statistics on screening throughput. Behaviour based frameworks enable metrics that better reflect structural risk, including:

• Proportion of exposure concentrated in sanctions sensitive sectors

• Frequency of indirect network linkages to higher risk entities

• Time between typology publication and internal detection capability update

• Ratio of behavioural escalations leading to substantive remediation

Such indicators demonstrate responsiveness and depth of understanding. They also align with the U.S. expectation that compliance programmes be risk based and tailored to the institution’s profile.

Organisational Considerations

Given the complexity of U.S. sanctions regimes, behaviour based capabilities are most effective when supported by cross functional collaboration. Sanctions policy, AML analytics, trade finance specialists, legal counsel, and model risk management must operate within a coherent governance structure.

Fragmentation of responsibility increases the risk that behavioural signals are misinterpreted or under-escalated. Clear accountability, supported by documented escalation pathways and senior management oversight, strengthens defensibility in the event of regulatory scrutiny.

A Defensive Imperative

The U.S. continues to set the global tone for sanctions enforcement. Institutions operating within or connected to the U.S. financial system must therefore align their control frameworks with an environment characterised by breadth, speed, and extraterritorial reach.

Behaviour based sanctions surveillance, supported by disciplined use of AI, provides a means of addressing structural evasion risk. It enhances the ability to detect patterns that do not trigger list matches but nevertheless create exposure.

In the context of the U.S., this evolution is less about innovation and more about expectation. Institutions that integrate behavioural analytics within robust governance frameworks will be better positioned to evidence credible compliance. Those that rely solely on static screening may find that enforcement developments outpace their controls.

The direction of travel is clear. Sanctions compliance in the U.S. increasingly demands understanding of architecture as much as identity. AI, when deployed transparently and proportionately, is becoming central to meeting that demand.