Sanctions & Watchlist Screening — for AI Agents, KYC & AML

Screen any person or company name against the world's major sanctions and watchlists in one call. Give this Actor a list of names and it returns, for each one, whether it matches a sanctioned party — with a risk score, the sanctions programs, aliases, dates of birth, nationalities and the source list behind every match.

Built for AI agents, KYC/AML onboarding, compliance tooling, and due-diligence — clean, structured, machine-readable output with zero setup and no API keys.

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Why this Actor?

Sanctions data is scattered across half a dozen government websites, each in its own awkward format — one ships CSV, another XML, another splits names and aliases across separate files. Keeping a live, unified, searchable copy is real work. And an LLM can't do this from memory: sanctions lists change weekly, and a wrong answer here is a compliance failure.

This Actor does that work for you. It pulls the latest official lists at run time, unifies them into one clean schema, and fuzzy-matches your names against every alias and spelling — including transliterations (e.g. Wagner ↔ Vagner, Cyrillic ↔ Latin).

• KYC / AML onboarding — screen new customers, suppliers or counterparties before you do business.
• AI agent compliance tool — let an agent check a name in real time via MCP and get back a structured verdict.
• Ongoing monitoring — re-screen your book of clients on a schedule as lists change.
• Due diligence & investigations — check the people and entities behind a deal against global sanctions.

Because the sources are public and this runs keyless, screening is fast and cheap, and always reflects the current lists.

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What it screens against

The Actor aggregates the major official, publicly-published sanctions lists:

Source	Coverage
US OFAC	Specially Designated Nationals (SDN) + Consolidated list
European Union	EU consolidated financial sanctions list
United Nations	UN Security Council Consolidated List
UK OFSI	UK consolidated list of financial sanctions targets

Together that's 30,000+ sanctioned people, companies and vessels, refreshed from source on every run so you always screen against the latest designations. You can screen against all of them or pick a subset.

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What you get

For every name you submit, the Actor returns one record:

Field	Description
query	The name you screened
isMatch	Whether any match was found (true / false)
riskLevel	none, low, medium or high — based on best match confidence
matchCount	How many matches were found
topScore	Confidence of the best match (0–100)
topMatchName	Name of the best match
topMatchSource	Which list it came from (OFAC / EU / UN / UK)
topMatchType	Person / Entity / Vessel
topMatchPrograms	Sanctions programs (e.g. RUSSIA-EO14024, DPRK3)
topMatchListedOn	Designation date
matchesSummary	Human-readable summary of all matches
matches	Full array of matches, each with score, source, programs, aliases, dates of birth, nationalities, addresses and a source link
screenedAt	Screening timestamp

The matches array is the detailed payload an agent or compliance system consumes; the flat topMatch* fields make the result easy to scan in a spreadsheet.

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Example output

{
  "query": "Vladimir Putin",
  "isMatch": "true",
  "riskLevel": "high",
  "matchCount": "3",
  "topScore": "100",
  "topMatchName": "PUTIN, Vladimir Vladimirovich",
  "topMatchSource": "OFAC",
  "topMatchType": "Person",
  "topMatchPrograms": "RUSSIA-EO14024",
  "topMatchListedOn": "",
  "matchesSummary": "PUTIN, Vladimir Vladimirovich (100%, OFAC: RUSSIA-EO14024) | Владимир Владимирович ПУТИН (100%, EU: UKR) | Vladimir Vladimirovich PUTIN (100%, UK: Russia)",
  "matches": [
    {
      "name": "PUTIN, Vladimir Vladimirovich",
      "score": 100,
      "source": "OFAC",
      "type": "Person",
      "programs": ["RUSSIA-EO14024"],
      "nationalities": [],
      "birthDates": [],
      "aliases": [],
      "sourceId": "...",
      "sourceUrl": "https://sanctionssearch.ofac.treas.gov/"
    }
  ],
  "screenedAt": "2026-07-06T15:00:00.000Z"
}

A clean name returns isMatch: "false", riskLevel: "none" — a positive "all clear".

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Input

Field	Type	Default	Description
queries	array	—	Required. Names of people or companies to screen.
minScore	integer	85	Minimum match confidence (0–100). Lower = more matches but more noise.
entityType	string	all	Restrict to person, entity, or vessel.
sources	array	all	Which lists to use: OFAC, EU, UN, UK.
country	string	—	Only return matches linked to this country / nationality.
maxMatchesPerQuery	integer	10	Max matches to return per name.

Example input

{
  "queries": ["Vladimir Putin", "Wagner Group", "Acme Trading LLC", "Jane Smith"],
  "minScore": 85,
  "sources": ["OFAC", "EU", "UN", "UK"]
}

Screening a whole customer list

{
  "queries": ["Customer One Ltd", "John A. Doe", "Global Imports SA", "..."],
  "minScore": 88,
  "entityType": "all",
  "maxMatchesPerQuery": 5
}

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How matching works

Sanctions screening is deliberately recall-first: it is far worse to miss a real sanctioned party than to surface a name that later turns out to be a different person. So the Actor:

1. Normalizes every name — case, punctuation and accents are stripped, so Kadyrov, Kadýrov and КАДЫРОВ all line up.
2. Matches against every alias, not just the primary name, including transliterations and alternate spellings.
3. Scores each candidate 0–100 using token and edit-distance similarity, and keeps those at or above your minScore.

Because of this, common names (e.g. "Maria Garcia", "James Wilson") can produce high-scoring matches even when your person is not the sanctioned one — simply because someone by that name is on a list. That's expected. Use the returned dates of birth, nationalities, programs and source links to confirm or dismiss a match.

Tuning tips:

• Raise minScore (e.g. 90–95) to cut noise when you screen common names.
• Add a country filter when you know the nationality.
• Pass entityType to separate people from companies.
• Include middle names / full legal names for sharper matches.

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How to use it

1. Click Try for free.
2. Paste the names into queries.
3. Click Start.
4. Read the results as JSON, CSV, or Excel, or via the Apify API.

A run screening 100 names against all four lists finishes in seconds.

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Calling from the API

curl -X POST "https://api.apify.com/v2/acts/YOUR_ACTOR_ID/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "queries": ["Vladimir Putin", "Acme Trading LLC"],
    "minScore": 85
  }'

Then fetch the dataset:

$curl "https://api.apify.com/v2/acts/YOUR_ACTOR_ID/runs/last/dataset/items?token=YOUR_APIFY_TOKEN"

The Actor also integrates with webhooks, Zapier, Make, and n8n, so you can wire screening straight into your onboarding flow.

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Use cases in detail

1. Customer & supplier onboarding (KYC/KYB)

Screen every new client, vendor or beneficial owner at onboarding. Feed the whole batch in one run and route any riskLevel of medium/high to a human reviewer.

2. AI compliance agents

Expose this as a tool to an AI agent so it can screen a name mid-conversation — "is this counterparty sanctioned?" — and get back structured, sourced results it can reason over.

3. Ongoing / periodic re-screening

Sanctions lists change constantly. Re-run your customer base on a schedule and catch newly-designated parties.

4. Transaction & trade compliance

Check parties to a payment, shipment or contract against sanctions before it goes through.

5. Investigations & journalism

Quickly check whether the people and companies behind a deal, property or network appear on any sanctions list.

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Output formats

Every run produces a dataset you can export as JSON, JSON Lines, CSV, Excel, HTML, or RSS, or pull via the Apify API into your own systems.

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Frequently asked questions

Which lists are covered? US OFAC (SDN + Consolidated), the European Union consolidated list, the UN Security Council Consolidated List, and the UK OFSI list — 30,000+ targets in total.

How fresh is the data? It's pulled from the official sources on every run, so you always screen against the current lists.

Does it match aliases and other spellings? Yes — it matches against all known aliases and handles accents and transliterations (Latin ↔ Cyrillic, etc.).

Why did a clearly-innocent name get a match? Because someone with a similar name is on a list. Screening is recall-first by design. Confirm identity using the dates of birth, nationalities and programs in the result.

Can I screen thousands of names at once? Yes. Batch them all into queries — the lists are loaded once per run and every name is checked against them.

Can an AI agent call this automatically? Yes — it's callable as a tool through Apify's agent/MCP integrations, returning structured JSON.

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Important — responsible use & disclaimer

This Actor aggregates publicly available sanctions and watchlist data published by government bodies, and provides potential name matches for informational purposes only.

• It is not legal, compliance, or financial advice, and it is not a substitute for a qualified compliance program.
• Matches are name-based and may include false positives (different people who share a name) or, in rare cases, miss records due to source gaps or spelling variance.
• Always verify a potential match against dates of birth, identifiers, and the official source list before making any decision about a person or business.
• You are responsible for using the results in line with applicable laws and regulations in your jurisdiction.

Data originates from official government sources (US OFAC, EU, UN, UK OFSI), which remain the authoritative references.

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Support

Want another list added (e.g. a national regime), or a field exposed differently? Open an issue from the Actor's page. Screen smart, stay compliant.