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Plain-English reference

What is fuzzy logic matching?

Approximate name matching that returns similar names even when the spelling is not identical, usually with a similarity score for review.

Last reviewed: 2026-10-03Not legal advice

Product context is educational relevance, not a feature-status or compliance claim.

Context: Visitor
Context: Education
Context: Trade
Context: Regulated Access
Screening
Identity Resolution
Front Desk
Compliance Manager
Admin

Visual Guide

How fuzzy matching should be used
  1. 1Similar name surfaced

    The matching engine returns a similarity signal for review.

  2. 2Review the source entry

    Open the official list record and the identifiers available for the listed party.

  3. 3Resolve identity

    Compare the available facts instead of treating the similarity score as proof of identity.

  4. 4Record the outcome

    Clear, escalate, or otherwise disposition the result with documented reasoning.

A similarity score helps reviewers find candidates. It is not a legal determination.

Plain-English Summary

Fuzzy matching broadens a name search beyond exact spelling so a screening process can surface transliterations, misspellings, reordered names, and other variations. OFAC’s own Sanctions List Search applies fuzzy logic only to the name field and returns a score representing similarity; other screening systems may use different algorithms and thresholds.

Why This Matters

Name screening has to account for variation, but similarity is not identity. A fuzzy score helps a reviewer find plausible candidates while still requiring comparison of the underlying list entry and available identifiers. OFAC does not prescribe one universal match threshold because the appropriate threshold depends on the facts, risk assessment, and compliance practices.

Explanation Depth

Concept Explanation

Fuzzy matching helps find names that are close but not identical. It can catch spelling changes and transliterations that exact matching would miss. The score tells you how similar the names are; a person still needs to decide whether the result is actually the same party.

When You'll See This in SecurePoint

Where a SecurePoint workflow exposes a match score, the score is review context rather than a regulator-determined outcome. Clearing, escalating, or otherwise dispositioning the case remains a recorded human step.

What You Should Do Next

Treat a fuzzy result as a lead for review. Open the complete source-list entry, compare available identifiers such as date of birth, nationality, identification numbers, address, or entity location, and document the basis for clearing or escalating the potential match under your organization’s procedures.

What Can Go Wrong

Using the score as a verdict creates both false-positive and false-negative risk. A high score is not proof that two parties are the same, and an aggressively high threshold can suppress relevant variants. Thresholds should be calibrated and tested against the organization’s risk profile rather than copied as a universal rule.

Sources & References

Related Terms

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