SecurePoint Trade
Four layers of intelligence. One confidence score.
Most screening tools do string matching. SecurePoint Trade combines phonetic, distance, similarity, and segment analysis to catch what simple searches miss — then tells your analysts exactly how confident the match is.
Phonetic matching catches sound-alike names across transliterations.
Levenshtein distance detects typos and character transpositions.
Jaro-Winkler similarity handles reordered name segments.
Trigram analysis scores partial overlaps in long entity names.
Entity Resolution Engine
Input entity
MOHAMMAD AL-RASHEED
Phonetic
Muhammed El-Rashid
Levenshtein
Mohamad Al-Rasheed
Jaro-Winkler
Mohammed Rasheed
Trigram
Al Rasheed Mohammad
Composite confidence
92%
4
Matching Algorithms
Phonetic, distance, similarity, trigram
0–100%
Confidence Score
Single unified output
Sub-second
Resolution Time
Per counterparty entity
100%
Explainability
Every score broken down
How each layer contributes to the final score
Each algorithm targets a different class of name variation. Together they produce a match confidence that simple screening cannot achieve.
Phonetic Matching
Sound-alike detectionCatches names that sound identical but are transliterated differently across languages and scripts.
Levenshtein Distance
Typo detectionMeasures character insertions, deletions, and substitutions to catch data-entry errors and fat-finger typos.
Jaro-Winkler
Transposition detectionOptimized for short strings and name prefixes. Catches character transpositions and partial matches.
Trigram Similarity
Segment reorderingBreaks names into 3-character segments and compares overlap. Catches reordered name components.
All four algorithms converge into
One Confidence Score
A single 0-100% Match Confidence score tells your analysts exactly how likely a hit is to be real — reducing false positives and accelerating clear decisions.
Score Transparency
Every match score is fully explainable
When a counterparty scores 92%, your analyst sees exactly which algorithms contributed and why. No black boxes.
Score Detail
Screened Entity
MOHAMMAD AL-RASHEED
Composite Confidence
92%
Why this matters
String matching misses 30-40% of true matches on transliterated names
Studies of sanctions screening accuracy show that simple string matching consistently fails on names transliterated from Arabic, Cyrillic, and Chinese scripts. Entity resolution with multi-algorithm scoring closes this gap — catching the matches that matter while reducing the false positives that slow your team down.
4
Algorithms combined
92%
Sample composite score
< 1s
Per-entity resolution
Explore More
Trade compliance capabilities
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Screen against OFAC, BIS, UN, EU, and UK HMT in a single pass.
Learn moreDiversion Risk Engine
Detect re-export routing and sensitive end-use patterns.
Learn moreAdverse Media
Discover enforcement actions beyond sanctions lists.
Learn moreEvidence Packs
Audit-ready documentation for every screening decision.
Learn moreWorkflow & Decisions
Clear, hold, and escalate with full audit trail.
Learn moreERP Integration
CSV and API intake from SAP, Oracle, NetSuite.
Learn moreFAQ
Entity resolution questions
For compliance teams evaluating screening accuracy and false positive reduction.
See entity resolution in action
Request a demo to see how 4-layer matching reduces false positives and catches what simple screening misses.