How Screening Algorithms Are Rewriting Trade Sanctions Compliance for Financial Institutions

Large volumes of trade and payments data now pass through automated sanctions filters long before any compliance officer sees a file. AI systems weigh fuzzy name matches, vessel histories and routing patterns in milliseconds, then decide which transactions deserve human attention. Regulators are starting to ask not only whether firms have screening in place, but whether they understand and control the algorithms that stand between them and an OFAC penalty.

Sanctions Compliance Shifts to Real Time

Sanctions have become one of the core tools of foreign policy rather than a niche adjunct to export control. The European Commission describes restrictive measures that range from asset freezes and travel bans to broader trade restrictions across entire sectors, all backed by a growing volume of guidance for firms that sit in the middle of those flows. The result is a sanctions landscape that changes often and rarely becomes simpler.

In the United States, the civil penalties page of the U.S. Office of Foreign Assets Control (OFAC) reads like a running ledger of control failures where standard screening tools could not keep pace with business growth or geopolitical shifts. Banks, insurers, payments companies and even nonfinancial firms have all paid for gaps in how customers, counterparties and transactions are checked against sanctions lists. At the same time, faster payment rails and higher trade volumes leave little room for batch screening that takes place hours after a transaction is booked.

OFAC’s Sanctions Compliance Guidance for Instant Payment Systems explicitly acknowledges the role of automation and AI in keeping up with real-time transfers. The document encourages modern tools where appropriate, while repeating a familiar principle that technology does not displace a risk based compliance program. The message is clear. Regulators expect sophisticated systems, but they are willing to treat those systems as part of the firm’s own judgment rather than an excuse.

Inside a Modern Screening Stack

Behind each hit or clear on a sanctions alert sits a stack of data, rules and models that looks very different from the static list matching of a decade ago. At the base are reference lists, including OFAC’s Specially Designated Nationals list, EU and UK sanctions lists, UN designations and commercial watchlists that aggregate corporate ownership and politically exposed person data. On top of that sit customer and counterparty records, payment messages and trade documents that can be screened for names, addresses, account numbers and vessel identifiers.

The Wolfsberg Group’s Guidance on Sanctions Screening describes this as a control that detects and manages sanctions risk across the customer lifecycle and transaction flow. Matching can be simple, such as an exact name comparison against a list. More commonly, it involves fuzzy matching that accounts for transliteration, initials, partial names and typographical errors. Modern platforms layer in additional signals such as date of birth, national identifiers and corporate registration details in order to reduce noise while preserving sensitivity to risk.

Alert handling is the second half of the system. First-line reviewers resolve straightforward false positives, escalating edge cases to specialist investigators who gather additional facts and decide whether to block, reject or allow a transaction. Each decision feeds back into the system, either through manual tuning of rules or through machine learning models that learn which combinations of features usually correspond to true matches. For trade finance and shipping, screening extends to vessels, ports, container routes and goods descriptions, building a multilayer view that is very different from a simple name search in a core banking system.

Geolocation and GPS data have become critical inputs for sanctions screening in maritime trade and insurance. Automatic Identification System data and satellite tracking enable firms to monitor vessel movements, identify port calls in sanctioned areas and detect ship-to-ship transfers that may indicate sanctions evasion. The rise of dark shipping practices, where vessels manipulate or disable their AIS transponders to conceal their locations and activities, has pushed compliance teams toward more sophisticated tracking tools that combine AIS data with satellite-based position validation and behavioral analysis to identify vessels engaged in illicit trade despite attempts to obscure their movements.

What OFAC Expects From Automated Filters

OFAC’s Framework for OFAC Compliance Commitments remains the anchor for sanctions compliance programs. It sets out five core elements, including management commitment, risk assessment, internal controls, testing and auditing, and training. None of those elements are written for a particular technology. Each applies equally to a spreadsheet of SDN names or to a cloud based screening engine that uses gradient boosted trees to score alerts.

The instant payments guidance adds a more specific layer for automated environments. OFAC notes that some institutions use AI and other advanced tools to supplement sanctions controls in fast-payment channels, then points back to the need for documented risk assessments, governance and testing of those tools. Screening may take place before, during or after payment execution, as long as the firm can show that the overall control environment adequately manages sanctions risk in light of speed and volume.

Recent enforcement actions reinforce the point that regulators care less about the label on a system and more about its performance and governance. OFAC civil penalty notices describe cases where firms relied on outdated screening versions, incomplete lists or systems that failed to cover all business lines. The narrative often highlights internal warnings that were not acted on or testing that documented weaknesses without triggering remediation. The underlying systems might use rules or machine learning, but the compliance story turns on whether leaders understood how those systems behaved and took responsibility for fixing them.

How Europe Designs Screening Rules

Europe approaches sanctions screening through a patchwork that is slowly turning into a more uniform framework. The European Banking Authority’s 2024 Guidelines on internal policies, procedures and controls for restrictive measures set common expectations for EU financial institutions. The guidance calls for up-to-date restrictive measures exposure assessments, clear allocation of responsibility, and documented controls that cover list management and screening tools.

In parallel, the European Commission’s sanctions portal explains how EU measures operate, including asset freezes and trade restrictions, and points to additional guidance on humanitarian exceptions and sector specific rules. For banks that operate across borders, the message is that sanction screening must be able to reflect both EU wide restrictions and member state specific implementation requirements.

The UK framework adds its own detail. The Office of Financial Sanctions Implementation publishes general financial sanctions guidance that describes compliance and enforcement expectations in plain terms. The Financial Conduct Authority has gone further, conducting a review of sanctions systems at more than 90 firms and publishing findings on good and poor practice. Those findings include detailed observations about data quality, list coverage, alert handling and the governance of changes to screening tools.

The FCA’s 2024 AI Update signals a further shift. The regulator describes a synthetic data tool for testing sanctions screening systems, a reminder that supervisors are prepared to use AI and data science to measure how well firms’ controls work.

The EU AI Act and Sanctions Screening

The EU’s Artificial Intelligence Act entered into force in August 2024 and introduces a risk-based framework that will affect how European financial institutions document, test and deploy AI in sanctions compliance. The Act classifies AI systems into risk categories, and many AI-based transaction monitoring and sanctions screening tools are expected to fall into the high risk category when they support regulated financial services and critical financial infrastructure.

Where AI-based sanctions screening and transaction monitoring tools fall within the Act’s high risk category, they must meet strict requirements for transparency, human oversight, data governance and model lifecycle management. Institutions will need technical documentation that describes how the system works, what data sets were used for training, the main logic behind automated decisions and how performance is monitored over time. The high-risk obligations become fully applicable in August 2026 for most systems, with an extended transition to August 2027 for certain already deployed high-risk systems and products.

For institutions operating across both sides of the Atlantic, this creates parallel governance obligations. While OFAC and the FCA focus on whether screening controls are effective and risk based, the AI Act adds specific technical requirements around explainability and documentation. Firms that already maintain model inventories and validation frameworks for credit or market risk will find familiar territory, but the EU framework is more prescriptive about what must be documented and how human oversight must be structured. Legal and compliance teams will need to work closely with data science and model risk functions to ensure that sanctions screening tools meet both the effectiveness standards set by financial regulators and the transparency standards required under the AI Act.

What Global Standards Say About AI Tools

Global standard setters do not write detailed sanctions screening rules, but they increasingly frame how supervisors and firms think about technology in financial crime controls. The Financial Action Task Force’s report on Opportunities and Challenges of New Technologies describes AI, machine learning and advanced analytics as tools that can make controls more effective, provided they are implemented within a robust risk based framework.

FATF’s Guidance on Digital Identity pushes institutions toward better use of digital identity systems for customer due diligence. Accurate and resilient identity data can directly improve sanctions screening by making it easier to distinguish genuine matches from common names and to resolve cross border aliases. Updated guidance on financial inclusion and AML/CFT likewise encourages risk-based controls that can operate at scale without excluding legitimate users.

Regional supervisors build on those principles. The Central Bank of the UAE’s guidance for licensed financial institutions on transaction monitoring and sanctions screening sets out expectations for governance, independent testing and board level oversight of automated tools. Similar documents in other jurisdictions mirror the language of FATF on governance and proportionality, while pressing institutions to invest in systems that can keep pace with rapidly changing sanctions regimes.

From Simple Name Matches to AI Risk Scoring

In practice, algorithmic sanctions screening covers a spectrum of tools rather than a single model. At one end sit deterministic rules that define thresholds for string similarity, name distance and the fields that must be screened. These engines are still the backbone of most sanctions programs, and they can be effective when lists and data are complete and well governed.

Machine learning usually enters as a way to manage volume and focus attention. Models can learn from historical alert data which combinations of features were associated with true matches and which were routine false positives. Vendors and banks describe approaches that assign scores to alerts or customers, prioritizing those that resemble past hits or that involve higher risk jurisdictions and products. Graph and network analytics add another layer by tracing links between entities, accounts and routes that might not appear suspicious when viewed in isolation.

Generative AI is starting to play a narrower but visible role. Firms experiment with using large language models to draft alert narratives, summarize regulatory updates or explain screening rules to internal audiences. Those use cases can reduce the burden on analysts, but they also raise familiar questions about hallucination, data protection and auditability if outputs are not carefully reviewed. Most regulators have not yet offered sanctions specific guidance on generative systems, which leaves institutions drawing on broader AI governance frameworks such as the NIST AI Risk Management Framework to structure their oversight.

Crypto Assets Add Screening Complexity

Crypto asset transactions present distinct challenges for sanctions screening systems that were built for traditional account based payments. The speed and finality of most blockchain transfers mean that preventative controls must operate in real time, because there is no practical ability to recall or reverse a completed on-chain transaction. Screening systems now have to monitor crypto wallet addresses alongside traditional identity markers and track assets as they move across chains through bridges, mixers and swaps.

OFAC has added crypto wallet addresses to the SDN List and brought enforcement actions against mixers and exchanges that failed to implement adequate controls, underscoring that sanctions rules apply as fully in the crypto sector as in banking. U.S. Department of the Treasury press releases on sanctions against crypto mixers and exchanges provide a public record of those enforcement trends. In the European Union, the recast Transfer of Funds Regulation extends the travel rule to crypto transfers and requires crypto asset service providers to collect and transmit originator and beneficiary information for most transactions, while MiCA brings those providers into a harmonised licensing and conduct framework. Regulation (EU) 2023/1113 on information accompanying transfers of funds and certain crypto-assets sets out the detailed requirements. Compliance teams increasingly pair blockchain analytics platforms with traditional screening tools so they can identify exposure to sanctioned addresses across multiple transaction hops and cross chain movements.

When Algorithms Miss or Muzzle Alerts

The central performance tradeoff in any screening system remains the balance between false positives and false negatives. Excessive false positives clog queues and slow genuine business. False negatives create the possibility of a violation that never reaches an analyst’s screen. Regulators have become wary of tuning projects and optimization efforts that focus heavily on reducing alert volumes without a clear articulation of residual risk.

FCA supervisors, for example, reported seeing firms with poorly documented change control processes where parameter adjustments significantly reduced alerts, yet there was little evidence of testing against realistic scenarios or back testing on historical data. OFAC’s enforcement files also highlight cases where internal teams identified gaps in screening coverage or data quality and raised concerns, only to see remediation delayed while transactions continued to flow.

For counsel, the technical language of precision, recall and receiver operating characteristic curves can be translated into more familiar concepts. What matters is whether the firm can demonstrate that changes to models and rules were justified, approved at the right level and tested in a way that aligns with the institution’s risk appetite. It is easier to defend a tuning decision that rests on a documented analysis of missed cases and post change performance than one that appears to have been driven by operational convenience.

Building a Defensible Screening Program

The governance questions around AI assisted sanctions screening are familiar to anyone who has worked with credit-risk or market-risk models. Institutions are expected to maintain an inventory of the models and complex rules that feed screening decisions, even when those models are embedded in vendor platforms. Each item in that inventory should have an owner, a description of function, a record of key assumptions and a history of changes and validation.

OFAC’s compliance framework and the EBA guidelines both stress governance structures that assign responsibility and ensure that senior management understands how controls work in practice. Model-risk management teams can play a role similar to their role in credit by testing sanctions models independently, reviewing data quality and validating performance against clear metrics. Internal audit functions provide another layer of assurance by testing adherence to policies and the completeness of coverage.

Vendor contracts sit at the center of many of these arrangements. Financial institutions rarely control the source code of their screening engines, but they can negotiate for transparency about how watchlists are sourced and updated, how algorithms operate and how changes are documented. Contracts can also include audit rights and cooperation obligations if regulators or law enforcement agencies need to understand how a particular decision was made. For multinational groups, those controls should be mapped against group sanctions policies so that a single customer or route is not treated differently in ways that create regulatory arbitrage.

How Screening Defects Show Up in Enforcement Files

Recent enforcement cases show how quickly sanctions screening issues can move from operational detail to public narrative. In the UK, the Office of Financial Sanctions Implementation fined Herbert Smith Freehills CIS LLP Moscow £465,000 in March 2025 after the firm made six payments totaling £3,932,392 to three sanctioned Russian banks during a wind down of local operations. OFSI highlighted deficiencies in sanctions checks and controls, underscoring that professional services firms are not immune from expectations traditionally associated with large banks. The payments were made to Alfa-Bank JSC, PJSC Sovcombank and PJSC Sberbank between May 25 and May 31, 2022 during the expedited closure of the Moscow office.

For U.S. institutions, OFAC civil penalty notices serve a similar function. They describe how clients and transactions slipped through controls because screening systems did not cover certain subsidiaries, ignored specific data fields or used outdated lists. In many cases, the narrative notes that the institution eventually upgraded tools and governance, then received a penalty that reflected both the severity of the violation and the quality of remedial steps. Voluntary self disclosure, thorough retrospective reviews and clear remediation plans remain important mitigants when screening defects surface.

Questions to Ask About AI Screening Tools

For counsel advising banks, fintechs, exporters or professional firms, the technical language around sanctions screening can be translated into a set of concrete questions. First, does the sanctions policy explicitly describe how automated screening tools operate, who owns them and how often they are tested. A policy that treats screening as a black box will be harder to defend than one that clearly sets out roles for compliance, risk, IT and model governance.

Second, is the firm confident that all relevant data is being screened against the right lists. That includes customers, counterparties, beneficial owners, vessels, ports and intermediaries, along with relevant message fields in payment and trade systems. Institutions should be able to explain how often lists are updated, how local sanctions regimes are incorporated and how data quality issues are identified and fixed.

Third, what does the documentation of models and rules look like. Even when a vendor retains intellectual property in their algorithms, clients can expect plain language summaries of how matching and scoring work, the main parameters that can be tuned and the way performance is measured over time. Independent validation reports should test those claims against realistic scenarios and historical cases, not just synthetic data or lab conditions.

Finally, how are incidents handled when problems emerge. Effective programs treat the discovery of a screening defect as a structured event that triggers escalation, internal investigation, retrospective reviews of affected populations and, where appropriate, engagement with regulators. As AI assisted tools play a larger role in sanctions compliance, the firms that fare best are likely to be those that treat algorithms as part of their own judgment and governance rather than as an external solution that stands apart from legal responsibility.

Sources

• Central Bank of the UAE: “AMLCFT Guidance for Licensed Financial Institutions on Transaction Monitoring and Sanctions Screening” (Sept. 8, 2021)
• European Banking Authority: “Guidelines on Internal Policies, Procedures and Controls to Ensure the Implementation of Union and National Restrictive Measures” (Nov. 14, 2024)
• European Commission: “Sanctions (Restrictive Measures)”
• European Commission: “Regulatory framework for AI” (AI Act)
• European Union: “Regulation (EU) 2023/1113 on information accompanying transfers of funds and certain crypto-assets” (June 9, 2023)
• Financial Action Task Force (FATF): “Opportunities and Challenges of New Technologies for AML/CFT” (July 1, 2021)
• Financial Action Task Force (FATF): “Guidance on Digital Identity” (March 6, 2020)
• Financial Action Task Force (FATF): “Guidance on Financial Inclusion and Anti-Money Laundering and Terrorist Financing Measures” (2025)
• Financial Conduct Authority: “Sanctions Systems and Controls: Firms’ Response to Increased Sanctions Due to Russia’s Invasion of Ukraine” (Sept. 6, 2023; last updated March 20, 2024)
• Financial Conduct Authority: “AI Update” (April 22, 2024)
• National Institute of Standards and Technology: “AI Risk Management Framework”
• Office of Financial Sanctions Implementation: “UK Financial Sanctions General Guidance” (published Feb. 13, 2024; last updated Nov. 18, 2025)
• Office of Financial Sanctions Implementation: “Imposition of monetary penalty: Herbert Smith Freehills CIS LLP Moscow” (March 20, 2025)
• U.S. Department of the Treasury: “Press Releases on Sanctions Against Crypto Mixers and Exchanges”
• US Office of Foreign Assets Control (OFAC): “A Framework for OFAC Compliance Commitments” (May 2, 2019)
• US Office of Foreign Assets Control (OFAC): “Civil Penalties and Enforcement Information”
• US Office of Foreign Assets Control (OFAC): “Sanctions Compliance Guidance for Instant Payment Systems” (Sept. 30, 2022)
• Wolfsberg Group: “Guidance on Sanctions Screening” (Jan. 21, 2019)

This article was prepared for educational and informational purposes only. It does not constitute legal advice and should not be relied upon as such. All cases, sanctions and sources cited are publicly available through official government publications and regulatory websites. Readers should consult professional counsel for specific legal or compliance questions related to AI use.

See also: Generative AI Forcing Legal Profession to Rethink the Billable Hour