Sanctions Screening Suite: OFAC, EU, UK, UN & PEP Watchlists

Sanctions Screening checks names, companies and crypto addresses against five official OFAC, EU, UK and UN sources in one call, and returns a plain-English decision your team can act on instead of a pile of raw rows to interpret yourselves. It does not provide PEP coverage.

1. Why use Sanctions Screening

AML and KYC teams doing this by hand open the OFAC SDN and Consolidated lists, the EU Consolidated list, the UK Sanctions List and the UN Consolidated list separately and manually cross-check every name against each one.

Enterprise AML/KYC screening platforms solve the coverage problem, but typically cost $5,000 to $100,000+ a year in licensing before you screen a single name. Most low-cost screening tools sit in between: they fetch one or two lists and hand you a flat list of fuzzy-match rows, leaving you to work out whether three "matches" are actually the same sanctioned person listed three times, whether a hit is a coincidence, and what to do next.

Sanctions Screening is built to close that gap for five direct official sources: cross-list identity consolidation, a documented CLEAR/REVIEW/ESCALATE decision instead of a bare score, and a per-run audit trail, at pay-per-use pricing instead of an annual contract.

2. Key features

• OFAC SDN screening: the US Treasury's Specially Designated Nationals list, matched with typo and word-order tolerance.
• OFAC Consolidated screening: the US Treasury's non-SDN consolidated list, covering additional sanctions programmes.
• EU Consolidated Financial Sanctions: the European Commission's official financial sanctions database.
• UK Sanctions List: the current FCDO source (the legacy API list key remains UK OFSI for compatibility).
• UN Security Council Consolidated List: the UN's official sanctions list.
• AML/KYC risk-programme flags: matches tagged IRAN, RUSSIA-EO14024, DPRK, CYBER, TERRORISM, PROLIFERATION, GLOBAL-MAGNITSKY and more.
• Cross-list identity consolidation: the same person listed by OFAC, the EU, the UK and the UN comes back as one match, not four rows to reconcile by hand.
• False-positive analysis: date of birth, country, nationality or identifier mismatches are surfaced explicitly against every match.
• Denied party crypto address screening: BTC, ETH and similar wallet addresses checked against OFAC's published digital currency address list.
• Whitelist memory: names or list IDs already cleared are suppressed with a documented reason instead of re-flagging every run.
• PDF audit certificates: an optional, per-run screening certificate documenting subject, lists, versions, method, threshold and verdict.

3. Who it's for

For compliance and risk teams. Screen a new corporate customer at onboarding and get a documented CLEAR / REVIEW / ESCALATE verdict with a PDF certificate for the file, instead of four separate list lookups and a spreadsheet note nobody can reconstruct a year later.

For finance and accounts payable. Check a payee before releasing a wire, including the crypto wallet address when that is the payment rail, so a blocked party is caught before the funds move rather than after.

For operations and procurement. Run a whole supplier list before a contract round, then re-run it on a schedule in monitor mode so a supplier who becomes listed after onboarding surfaces on the next check rather than at audit.

For developers and AI agents. Call it as one step inside a KYC intake or payment-release flow: submit the subject, branch on verdict, and escalate only ESCALATE and REVIEW to a human — no person copying names between systems.

When to use it, and when not to

Use it for AML/KYC sanctions screening, denied-party and export-control checks, payment and payee screening, periodic re-screening, and pulling clean structured official-list data into your own systems. Use a separately licensed provider when PEP coverage is required.

Do not use it as your compliance programme. It is a screening input, not a determination: it does not decide whether to onboard, block or file a report, it does not replace analyst judgement on REVIEW and ESCALATE verdicts, and it is not legal advice. It also does not do adverse-media screening or full beneficial-ownership tracing — see Limitations for exactly where the boundaries sit.

4. Built for humans and AI agents

Run it in the Apify Console like any other tool: pick a mode, enter subjects, click Start. No setup, no API key handling, results in a browser table.

It is also a directly callable MCP tool, so an AI agent (Claude, Cursor, or any MCP-compatible client) can run a sanctions check or PEP watchlist screen as one step inside a larger workflow, such as KYC intake, vendor onboarding, or payment release, without a human copying names between systems.

One example agent call:

Tool: apifmcpfactory/sanctions-screening (via https://mcp.apify.com)

Input:
{
    "subjects": [{ "name": "Jane Doe", "country": "Cuba" }],
    "threshold": 85
}

Result (one item):
{
    "subject": "Jane Doe",
    "verdict": "REVIEW",
    "recommendedAction": "Route to a compliance analyst for manual review before proceeding.",
    "matchCount": 1,
    "highestConfidence": 91
}

5. How to use Sanctions Screening

In the Apify Console: pick a mode (screen is the default), fill in your subjects, and click Start. Two working demo subjects are pre-filled so a first run needs no editing.

Python:

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("apifmcpfactory/sanctions-screening").call(run_input={
    "subjects": ["AeroCaribbean Airlines", {"name": "Jane Doe", "country": "Cuba"}],
    "threshold": 85,
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["subject"], item["verdict"])

JavaScript:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('apifmcpfactory/sanctions-screening').call({
    subjects: ['AeroCaribbean Airlines', { name: 'Jane Doe', country: 'Cuba' }],
    threshold: 85,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

cURL:

curl "https://api.apify.com/v2/acts/apifmcpfactory~sanctions-screening/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"subjects": ["AeroCaribbean Airlines", "Acme Test Company"], "threshold": 85}'

6. Input parameters

Field	Type	Description
mode	string	screen (default), monitor, export, list_status, or refresh_cache.
subjects	array	Names as plain strings, or objects: {name, entityType, yearOfBirth, dob, country, nationality, idNumber, passport, regNumber, lei, program}.
subjectsText	string	Alternative to subjects: one name per line, or a CSV block with a header row. The combined subject limit is 100.
subjectsFileUrl	string	Alternative to subjects: a public URL to a .csv or .xlsx file of subjects. The combined subject limit is 100.
entityType	string	any (default), person, or org. A per-subject entityType overrides this.
threshold	integer	Minimum fuzzy-match score, 0 to 100. Default 85.
fuzzy	boolean	Typo/word-order/transliteration-tolerant matching. Default true.
lists	array	Restrict screening to specific lists. Default: all five official sources. UK OFSI is retained as the API key for the current UK Sanctions List. OpenSanctions requests fail explicitly as unavailable.
whitelist	array	Names or list entityIds from prior decisions to suppress.
generateCertificate	boolean	Render a PDF audit certificate for this run. Default false.
priorRunId	string	Monitor mode only: a prior run's ID to diff against.
webhookUrl	string	Monitor mode only: POST the change list here if any changes are found.
exportList	string	Export mode only: which list to dump.
exportFormat	string	Export mode only: csv (default), json, or xlsx.

7. Output

One dataset row per screened subject in screen mode:

{
    "subject": "Jane Doe",
    "verdict": "REVIEW",
    "recommendedAction": "Route to a compliance analyst for manual review before proceeding.",
    "priorityScore": 78,
    "matchCount": 1,
    "highestConfidence": 91,
    "narrative": "\"Jane Doe\" matched \"Jane A. Doe\" (91/100, strong-fuzzy) on OFAC SDN, EU Consolidated. This match requires analyst review before any decision is made.",
    "matches": [
        {
            "matchedName": "Jane A. Doe",
            "confidence": 91,
            "matchType": "strong-fuzzy",
            "sources": [
                { "list": "OFAC SDN", "entityId": "OFAC SDN-12345", "program": "CUBA", "listVersion": "2026-01-01T00:00:00.000Z", "sourceUrl": "https://sanctionslistservice.ofac.treas.gov/..." },
                { "list": "EU Consolidated", "entityId": "EU Consolidated-987", "program": "Cuba", "listVersion": "2026-01-01T00:00:00.000Z", "sourceUrl": "https://webgate.ec.europa.eu/..." }
            ],
            "riskIndicators": [{ "code": "CYBER", "label": "Cyber-related sanctions programme" }],
            "falsePositiveAnalysis": { "mismatchSignals": [], "likelyFalsePositive": false, "reason": "No contradicting attributes found; name match alone should not be dismissed without analyst review." },
            "autoCleared": false,
            "ownershipRisk": { "flagged": false, "linkedEntities": [], "note": "No ownership/linkage signal found in the source list data for this entry. This is not a full 50%-rule check; beneficial-ownership tracing requires external corporate-registry data this tool does not have." }
        }
    ],
    "whitelisted": false
}

Field definitions

Field	Meaning
verdict	CLEAR, REVIEW, or ESCALATE.
recommendedAction	Plain-English next step and escalation routing.
priorityScore	0 to 100, weighted by confidence, ownership signal, and high-risk programme flags.
matchCount	Number of consolidated (not raw per-list) matches above threshold, excluding auto-cleared.
highestConfidence	Highest score among all matches, including auto-cleared ones.
narrative	Reasoning in plain English, including for a clean result.
matches[].sources	Every list this consolidated identity appears on.
matches[].riskIndicators	Programme-category tags parsed from the matched list entries.
matches[].falsePositiveAnalysis	Concrete attribute mismatches (DOB, country, identifier) versus the subject's own stated data.
matches[].autoCleared	True when a low-confidence match is contradicted by two or more subject attributes.
matches[].ownershipRisk	50 percent rule linkage signal from the source list data, with an explicit non-overclaim note.

8. Use cases

• Onboarding screening: check a new customer or counterparty before opening an account.
• KYC intake: run an AML watchlist check as part of a standard know-your-customer flow.
• Payment screening: check a payee before a wire, including a crypto wallet address if that is the payment rail.
• Vendor and supplier due diligence: screen a supplier list in bulk before signing a contract.
• Denied party screening for export control: check a buyer or intermediary against sanctions lists before shipping.
• Periodic re-screening: use monitor mode on a schedule to catch subjects who become newly listed after onboarding.
• Marketplace and platform trust and safety: screen sellers or partners before activating an account.
• Building your own compliance tooling: use export mode to pull clean, structured list data into your own systems instead of maintaining five official-source parsers.
• AI agent workflows: an agent handling KYC intake or invoice payment can call this tool directly via MCP as one step in a larger flow.

9. How it works

subjects (names / crypto addresses)
        |
        v
 parse & normalize  ----  accepts array, text, CSV, or XLSX
        |
        v
 fetch + cache lists  ----  OFAC SDN/Consolidated, EU, current UK Sanctions List, UN
        |                   (streamed + parsed incrementally, cached once per day)
        v
 fuzzy match  ----  deterministic, transliteration-aware, per list
        |
        v
 cross-list consolidation  ----  same identity across lists becomes one match
        |
        v
 risk flags + false-positive analysis + ownership signal
        |
        v
 whitelist suppression
        |
        v
 verdict + narrative + audit summary (+ optional PDF certificate)

10. Performance and cost

Item	Typical
Screening, cached lists	Sub-second per subject once lists are cached.
First run or cache refresh	A few minutes, dominated by downloading and parsing the five official files.
entity-screened event	Charged once per non-empty subject, screen and monitor modes alike.
pdf-certificate event	Charged once per run when generateCertificate is true and the certificate is actually produced, regardless of subject count.
list-exported event	Charged once per export-mode run, regardless of list size or format.
Memory	Runs at 4 GB by default; official lists are streamed and parsed incrementally.

Wire mode: "refresh_cache" to a daily Apify Schedule so ordinary screening calls read from cache instead of re-downloading and re-parsing every list on every call.

11. Limitations

• The OFAC 50 percent rule signal is a signal, not full ownership tracing. It surfaces only what a source list's own data already states about linked entities. It does not compute beneficial-ownership percentages, and it does not trace ownership through corporate-registry data this tool does not have.
• No adverse-media data. This checks structured government and watchlist sources only. It does not search news, litigation, or adverse-media coverage.
• UK and EU date-of-birth mapping is best effort. The current UK Sanctions List parser maps available DOB and identifier fields; EU DOB and identifier extraction remains best effort. Treat those attributes as supporting context, not sole decision criteria.
• Decisions need human review. A REVIEW or ESCALATE verdict is a prioritised starting point for an analyst, not a final compliance decision, and a CLEAR result means no match was found above your chosen threshold across the lists screened as of the date shown, not a guarantee.
• No PEP coverage. OpenSanctions is not downloaded or used because this Actor has no commercial data licence for those feeds. Explicit legacy OpenSanctions requests fail without producing a screening result.
• Transliteration covers Cyrillic and Greek only. Arabic, Hebrew and CJK-script names are matched only against whatever Latin rendering the source list already provides.

12. Responsible use

This tool is built for legitimate AML, KYC sanctions and denied-party screening workflows. It reads only direct official sources and stores nothing beyond your own run's output. It is not a substitute for a qualified compliance programme, and it is not legal advice. A screening result here should be reviewed by a person before any account, payment, or business decision is made on it, particularly for REVIEW and ESCALATE verdicts.

13. FAQ

What is a denied party or watchlist check? Screening a name or company against government-published sanctions, embargo, and denied party lists (OFAC, EU, UK, UN and aggregated watchlists) before doing business with them, so you avoid transacting with a sanctioned entity.

What is the OFAC SDN list? The US Treasury's Specially Designated Nationals list: individuals and companies US persons are generally prohibited from dealing with.

What is the difference between OFAC SDN and OFAC Consolidated? SDN is the primary blocked-persons list. Consolidated covers additional non-SDN sanctions programmes such as sectoral sanctions and other restricted-party lists.

What is the UK OFSI list key? It is the backwards-compatible API key for the current FCDO UK Sanctions List. The retired OFSI Consolidated List is not used.

Does this cover PEP screening? No. This Actor has no commercial OpenSanctions licence and does not download or use those feeds.

Is this an AML screening tool or a KYC tool? Both. The same check supports AML watchlist screening at onboarding or before a transaction and ongoing KYC due diligence via monitor mode.

Why does the same name sometimes get one match instead of several? Because the same real-world person or company is often listed independently by more than one list. This tool consolidates those into one match with every source listed, rather than returning one row per list.

What does auto-cleared mean? A match scored below strong-confidence and contradicted by two or more of the subject's own stated attributes (date of birth, country, or identifier). It is still shown, just flagged so an analyst can deprioritize it.

Is a CLEAR result guaranteed accurate? No. It means no match was found above your chosen threshold, across the lists screened, as of the date shown. Lower the threshold to catch more spelling and name-order variants at the cost of more false positives to review.

Can I re-check the same subjects without re-reading everything? Yes, use mode: "monitor" with priorRunId set to a previous run's ID; you get back only what changed.

Does this screen crypto wallet addresses? Yes. A subject value shaped like a BTC, ETH or similar address is checked against OFAC's published digital currency address list instead of fuzzy name matching.

Does this store any of my data? No. Screening reads only the official public sources listed above and writes only this run's own output.

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