Sanctions Screening: What “Good” Looks Like

All of this puts more pressure on the teams responsible for financial crime screening. A single sanctions designation can significantly increase the number of entities that need to be screened, and in turn can significantly increase the alerts that need attention. Regulators are also setting more detailed guidance on how screening controls should be designed, tested and governed.

Screening in 2026 and beyond must be effective, efficient and explainable, in order to help fight financial crime and maintain regulator satisfaction. Good screening is based on three core factors: data quality, match confidence and robust alert handling.

Laying the foundation: data quality

Effective screening starts with reliable data. Poor data quality can undermine even the most advanced screening technology, leading to excessive false positive alerts or missed true hits.

So, what constitutes poor data? Common issues include:

• Inaccurate data, which can be caused by a multitude of reasons, including manual data entry
• Missing or incomplete data, including customer records
• Inconsistent formats
• Duplicate data

Poor data makes it more difficult for a screening system to make matches, leading to high false positive rates which can overwhelm teams, or true hits being missed, creating significant risk exposure.

Improving data quality is the first step in helping improve the screening process and taking a coordinated approach across an organization. Key steps include standardizing data capture, implementing consistent formatting, helping determine how to manage language differences and transliteration, and setting clear data ownership and governance.

Increased efficiency – improving match confidence

One of the biggest challenges for compliance teams is balancing regulatory expectations with internal budget and resource constraints. They need a screening system that helps strike this balance, identifying risk without overwhelming teams with irrelevant alerts.

To help increase match confidence, modern screening systems may combine multiple techniques including fuzzy matching techniques. These help to detect variations in names, languages and formats that occur across global watchlists and internal systems.

Common fuzzy matching algorithms include:

• Levenshtein Distance: This calculates single-character edits to catch typos (e.g. Jon Smith vs. John Smith). If it is the same person, systems set to catch exact matches only would miss this hit. Levenshtein catches it.
• Phonetic Matching: Matches names that sound similar (e.g. Smith vs Schmidt).
• Jaro-Winkler Similarity: Gives extra weight to similarities at the beginning of strings; useful for abbreviations (e.g. Mohammed vs. Mohamad).
• Tokenization: Breaks names into components to account for missing or extra name parts (e.g. Ali Hassan Mohamed vs. Mohamed Hassan Ali).
• Normalization and Transliteration: Standardizes data by removing accents and converting text to a single alphabet (e.g. Habimana vs. Habîmana).
• Alias Libraries: Pre-built variant databases for high-profile entries which may have different names. For instance, Muammar Gaddafi appears on the OFAC SDN list under 40+ name variants.

There is no single configuration that will suit every organization. Matching thresholds and logic should reflect your risk profile and business model, and you must be able to explain your screening approach and decision making to regulators.

Increasing effectiveness – robust alert handling

Even the most effective screening system will generate alerts which need reviewing. The true test often lies in how these are handled.

Alert handling is the most resource-intense part of the screening process for many organizations. Analysts must review alerts, gather additional information and make decisions, often very quickly.

Automation can help to reduce manual effort, but must be implemented carefully to maintain oversight and accountability.

Weaknesses in alert review can undermine an otherwise strong screening program. These may include:

• Alerts being dismissed without following defined procedures
• Decisions being made lacking sufficient documentation
• Inconsistent processes being followed

Even if the decision made is the correct one, the process must be explainable and defensible to regulators. This is where clear governance, processes, training and ongoing internal reviews are key. 

The evolving role of AI

Artificial intelligence (AI) is increasingly being explored as a way to improve both effectiveness and efficiency. In practice, AI may be most effective in areas such as helping:

• Data enrichment and entity recognition
• False positive reduction
• Alert triage and prioritization
• Automated narrative generation

AI can help process large volumes of data and identify patterns that might not be immediately obvious. That said, regulatory expectations are clear: organizations are accountable for their screening process and outcomes, regardless of the technology used. 

If AI is deployed, clear guardrails are needed. Organizations must understand how models operate (ie. no “black box” approach), regularly test and validate models and help maintain documentation for auditing purposes.

AI shouldn’t be viewed as a silver bullet in sanctions screening. While AI offers opportunities, rules-based or deterministic approaches may be more explainable and cost-effective; either way, human oversight and accountability remains key.

Screening fit for the future

Sanctions screening is the backbone of any compliance program, helping organizations  to meet regulatory expectations and maintain the integrity of the global financial ecosystem. 

As expectations evolve, compliance teams will need to continue to adapt. Of course, this isn’t a simple task, but by focusing on data quality, match confidence and robust alert handling, organizations can strengthen screening frameworks and respond more effectively to the changing risk environment.

To explore how advanced data, analytics and screening capabilities from LexisNexis® Risk Solutions can support your sanctions screening process, contact our team.

References:

1. LexisNexis® Risk Solutions. Sanctions Pulse Full-Year 2025. LexisNexis Risk Solutions, 2025. Taking the Pulse of Major Sanctions. 
2. Office of Foreign Assets Control. Entities Owned by Blocked Persons (50% Rule). 2020. Office of Foreign Assets Control. Entities Owned by Blocked Persons (50% Rule).