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The DEV Community article “I Ran 1,000 Names Through 2 OFAC APIs. 80 Hits Disagreed.” reports that two commercial sanctions-screening APIs returned different risk verdicts for 80 flagged hits when the author screened 1,000 common Eastern European and Central Asian names. The figures are the author’s account of a single run. They have not been independently reproduced, and the second provider is not named. Despite the title’s reference to OFAC, both tools are described as commercial APIs, not OFAC’s own search.
The run is still useful. It shows that a screening result depends on the matching method, list coverage and threshold of the tool that produced it, and that a fuzzy name hit is an alert to review, not a finding about a person.
What the author reports
The article’s method is short, and most of its claims rest on it. The table separates what the author states from what a reader can check. Every figure comes from the article itself.
| Element | As reported | What to keep in mind |
|---|---|---|
| Publication | DEV Community article by Onizuka, shown as published September 26, 2026 | A self-reported account; no independent reproduction |
| Sample | 1,000 common Eastern European and Central Asian names | The full name list is not published |
| Tools | Two commercial sanctions-screening APIs | The second provider is unnamed; neither is identified as OFAC’s own search |
| Scope | Individuals only | Entities were excluded from the test |
| Threshold | 0.7 | The author’s setting; OFAC sets no universal threshold |
| Headline result | 80 divergent risk verdicts among flagged hits | The denominator is flagged hits, not all 1,000 names |
| Worked example | “Sergei Ivanov”: one API returned 101 total matches across OFAC, UN and EU lists; the second API returned 23 flagged matches | The two counts are described on different bases; not independently verified |
| Response excerpt | One exact OFAC SDN match alongside numerous fuzzy matches, including one fuzzy result whose explanation reportedly showed no shared tokens | An excerpt from the author’s output, not an authenticated log |
The article does not publish a complete result set, a protocol, or an auditable table. The numbers therefore cannot be checked from the piece alone, and they should be read as the author’s observations.
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Why 80 is not a percentage of 1,000 names
The headline’s 80 counts divergent risk verdicts among flagged hits. It does not say that 80 of the 1,000 names produced different decisions. Restating it as “8% of all names” would misread the experiment. The reported figures also do not give the total number of hits each tool flagged, and without that denominator no meaningful percentage of the sample can be derived.
What the Sergei Ivanov example can and cannot show
The author uses “Sergei Ivanov” to illustrate the gap: one API is reported to return 101 total matches across OFAC, UN and EU lists, while the unnamed second API returned 23 flagged matches. Three cautions apply.
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- The two counts may measure different things. As described, one is a total of matches across three lists and the other is a count of flagged matches. Unless the definitions match, the gap cannot be read as a like-for-like difference.
- List coverage is not shown to match. The article does not establish that the second provider screens the same lists, so part of any gap may reflect scope rather than matching quality.
- A common name is an illustration, not a finding. A count of list matches for a widely shared name says nothing about any individual who bears it.
The response excerpt adds two more observations, but as excerpts they show what the author says the tool returned, not how the tool behaves in general.
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Sanctions name screening is probabilistic. A tool does not answer whether a name belongs to a listed person. It estimates how similar an input string is to list entries and returns the entries above a cutoff. Several factors can make two tools diverge on the same names.
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Fuzzy matching uses different techniques
OFAC’s own Sanctions List Search, available through its Sanctions List Service, illustrates how this works. OFAC states that only the name field invokes fuzzy logic; other fields use character matching (OFAC FAQ 246). Its score explanation names Jaro-Winkler and Soundex, says the search compares complete strings and split name parts, and returns the higher score from those approaches (OFAC FAQ 249). This shows that fuzzy search can surface candidates through several techniques. It does not reveal how either commercial API in the article scores names, and the article does not disclose either provider’s method.
Transliteration and aliases multiply candidates
Names from Eastern Europe and Central Asia are often rendered in Latin script with more than one accepted spelling, and lists carry alternate spellings as aliases. OFAC’s Advanced Sanctions List data model can provide additional metadata, support multiple languages and character sets, and identify weak aliases as distinct from strong ones (OFAC’s Advanced Sanctions List Standard FAQs). Two tools that treat aliases or weak aliases differently can return different candidate sets from the same input.
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Common names produce more near-matches
This point is an editorial inference, not a finding in the article. The sample was built from common names, and shared surnames and given names generate many near-identical entries. That is the condition under which thresholds and tie-breaking rules matter most. A test on common names is a stress test, and its results should not be generalized to rare names or to a customer base with different characteristics.
Why 0.7 is a setting you choose, not a standard
The article’s test used a threshold of 0.7. That is the author’s parameter, not an OFAC-approved default. OFAC does not set a universal match threshold; it says users must determine thresholds based on their internal risk assessments and established compliance procedures (OFAC FAQ 250). Asked whether it recommends a specific threshold, OFAC answered: “OFAC cannot make such a recommendation because each search has its own unique set of facts surrounding it.”
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A shared number does not guarantee shared behavior if two tools calculate scores differently, and the article does not examine how each provider calculates its score.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to verify an OFAC name match
OFAC’s guidance on investigating a potential match is the most practical check available, and it applies whatever tool produced the hit. The steps below follow OFAC’s advice in OFAC FAQ 5; the sequence is an editorial synthesis of that guidance.
- Treat the hit as an alert. OFAC warns that many potential matches are false positives. A name-only hit is a reason to investigate, not proof of identity.
- Open the complete listing entry. Do not decide from the summary line a screening tool displays. OFAC’s Sanctions List Service provides current list data for download, so the entry can be checked against the official source. Note any program tags on the entry, because OFAC directs users to them when determining how to treat a true hit.
- Compare identifiers. Set the listing’s aliases, nationality, identification numbers, date and place of birth, and addresses against the customer or transaction record. A name match with no agreeing identifiers is weak evidence; one with several agreeing identifiers justifies escalation.
- Record what agreed and what did not. Keep the fields compared, the threshold in use, and the reviewer’s decision. This is the evidence trail that auditors and compliance teams will expect to see.
- Escalate under your own policy. OFAC says organizations should follow their own sanctions compliance policies. The decision on a possible true match belongs to that process, not to a tool’s score.
The article closes by asking which secondary check teams forget after a fuzzy HIGH match. OFAC’s guidance points to the complete listing entry and the identifier comparison, not to a second API. OFAC does not prescribe API redundancy, so whether a second tool adds value is a decision for your own risk assessment.
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Match counts alone do not show which tool is better. The comparison that matters is along the axes below. The first four are grounded in OFAC’s descriptions of list data, metadata and aliases; the framework itself is an editorial synthesis. The article does not disclose its second provider, so each vendor’s features must be verified individually.
Quick Recap
| Axis | Why it matters | Questions to ask a vendor |
|---|---|---|
| Lists and jurisdictions | A name can appear on one list and not another; the article’s worked example spans OFAC, UN and EU lists | Which lists are covered, and are they screened together or separately? |
| Data source and update cadence | Stale list data can miss recent designations | Where does list data come from, and how often is it refreshed? |
| Aliases, transliteration and character sets | Names from this region are often transliterated and carry several spellings | Are aliases returned, are weak and strong aliases distinguished, and which scripts are supported? |
| Returned identifiers and explanations | A reviewer needs to see which fields matched and why a candidate scored as it did | Does each hit show the matched fields and a score explanation? |
| Threshold controls | Settings change how many candidates appear | Can thresholds be set per list or per name type, and is the default documented? |
| Review workflow and audit logging | Compliance decisions need a record of who decided what, and when | Are dispositions, reviewers and timestamps retained, and for how long? |
What the experiment does and does not establish
- It supports: two commercial tools can return different verdicts on the same names, and list scope, thresholds and matching methods are the variables to examine first.
- It does not establish: an error rate for either tool, which tool was correct in any of the 80 cases, or any false-positive or false-negative count. The article frames the divergences as differing risk verdicts, and they should be read that way.
- It does not establish: that OFAC requires more than one screening API. OFAC’s published guidance does not prescribe that.
- It does not establish: that OFAC’s public search is designed or suitable for continuous automated use. Any such use requires separate verification of what is permitted.
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