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Brighter - Revolutionising Sanctions Screening with AI and Machine Learning
Darren TempleMay 2, 2025 9:00:00 AM4 min read

How Global RADAR Is Revolutionising Sanctions Screening with AI and Machine Learning

In today’s volatile geopolitical and regulatory landscape, financial institutions and multinational organisations face mounting pressure to comply with sanctions regulations across multiple jurisdictions. Traditional sanctions screening methods, often rule-based, manual, and heavily reliant on clean data inputs, are no longer sufficient. The integration of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) is transforming how we approach this challenge, from identifying targets in messy data to reducing costly false positives.

This blog outlines why Brighter Consultancy supports the adoption of Global RADAR’s platform, specifically designed to tackle these challenges, and highlights the key benefits it offers.


1. Turning Unstructured Data into Screening Intelligence

Sanctions screening usually begins with identifying who or what needs to be screened. But real-world data isn’t always clean or well-structured. It may appear in unformatted documents, emails, contracts, or other unstructured sources, and in various document formats, such as PDF, Word, Excel, TIFF, etc. This is where NLP plays a vital role. NLP and ML models can extract names, entities, registration numbers, and even context from large volumes of unstructured content.

By doing so, AI helps automate the identification of screening candidates, significantly reducing manual pre-processing work and ensuring no relevant parties are missed due to data quality or format inconsistencies.  We have observed efficiency gains of up to 30,000%, making the process up to 300 times faster than traditional methods, and accuracy improvements of up to 300% with our unique approach.


2. Cleansing and Enriching Data for Accuracy

Once entities are identified, both the data to be screened and the reference sanctions lists (like OFAC, UK, UN, and EU consolidated lists) must be cleaned and standardised. This stage is critical; poor-quality input leads to poor results, including false matches or missed threats.

AI-driven cleansing involves multiple techniques:

  • Normalisation: Removing punctuation, excess whitespace, HTML codes, control characters, and diacritics.
  • Noise word filtering: Stripping titles like “Mr,” “Ltd,” or “Inc” based on whether the subject is a person or an entity.
  • Linguistic enrichment: Using AI to generate alternate name forms through nickname databases (e.g., “Dave” for “David”) and handle linguistic anomalies like apostrophes, dashes, or ligatures.
  • Duplicate removal and smart splitting: AI identifies and corrects over-merged entities or names with embedded project codes or locations.

Our revolutionary approach ensures data is not only cleaned but intelligently transformed to make matches more precise.


3. Advanced Matching: Combining Distance Metrics and Phonetics

With clean data in hand, our system then attempts to match the search target against the sanctions list. AI enhances this step using a blend of exact and fuzzy matching techniques:

  • Jaro-Winkler Distance: Scores similarity based on common prefixes and transpositions. Particularly effective for identifying typos or name ordering variations.
  • Levenshtein Distance: Calculates how many character edits are needed to turn one name into another—great for catching common misspellings.
  • Metaphone 3 Phonetic Matching: Encodes names by how they sound, catching homophones and regional pronunciation variants (e.g., “Mohammad” vs. “Muhammad”).

The combination of these methods allows for a broad but controlled search net, ensuring that even cleverly disguised or inconsistently spelt names are surfaced. These techniques are integral to our platform to match names while preserving system performance.


4. Reducing False Positives with AI Intelligence

One of the most persistent problems in sanctions screening is the high volume of false positives, which are alerts that appear suspicious but ultimately prove to be irrelevant. AI can dramatically reduce this burden by applying contextual filters and learning from previous alert outcomes.

Key AI-driven strategies include:

  • Relevancy scoring: Matches are ranked by their contextual likelihood using weighted scoring (e.g., placing less weight on common words like “Bank” or “Group”).
  • Alert history analysis: The system learns from previous reviews and recognises recurring false positives. If a name pair has been cleared before, AI can suppress or flag it for lower-priority review.
  • Threshold tuning: Machine learning models analyse alert patterns and suggest more precise cutoff scores for automatic rejection or escalation.

Using past resolution data allows systems to become smarter over time, reducing both user fatigue and compliance risk.  We have reduced escalation rates to as low as 0.2% - that is as few as 10 escalations for every 10,000 searches, meaning the Compliance resource is focused on real risks.


The Payoff: Smarter Compliance, Reduced Cost, Greater Confidence

AI-powered sanctions screening offers tangible benefits:

  • Increased detection rates: AI identifies complex, non-obvious matches that traditional tools miss.
  • Fewer false positives: Analysts spend less time chasing dead ends and more time investigating real risks.
  • Regulatory alignment: Systems evolve to meet new sanctions regimes quickly and effectively.
  • Operational efficiency: Automation reduces time-to-decision and lowers cost per screening.

In a world where sanctions lists evolve rapidly and evasion tactics become increasingly sophisticated, AI and ML are no longer optional; they are essential. By combining intelligent data extraction, powerful matching algorithms, and continuous learning, our modern sanctions screening systems deliver both compliance and peace of mind.

If you're aiming to create a more intelligent, resilient, and compliance-driven business, Global RADAR offers a "no-regret" solution—marking the starting point for improved client satisfaction, faster and more accurate operations, and reliable compliance.
For more information, please visit: BRIGHTER CONSULTANCY

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Darren Temple
Darren brings over 30 years of experience in Financial Services, with a focus on regulatory remediation, financial crime, and operational transformation. He has led large-scale delivery teams across insurance and banking, including Financial Crime and Sanctions programmes for some of the UK’s largest institutions. Darren combines strategic oversight with deep hands-on expertise to help Brighter shape and deliver innovative, tech-enabled compliance solutions.
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