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Wanted List Screening: FBI, Interpol & Europol

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Wanted List Screening: FBI, Interpol & Europol

Screen individuals against public criminal wanted lists from three major law-enforcement agencies — FBI, Interpol, and Europol — in a single run. Designed for KYC workflows, compliance screening, OSINT investigations, and journalism.

What it does

The actor pulls public wanted-notice data from:

  • Interpol — Red Notices via the public REST API (ws-public.interpol.int)
  • Europol — EU Most Wanted listings via eumostwanted.eu
  • FBI — Most Wanted, Cyber, Counterterrorism, and other categories via fbi.gov/wanted/api/

It operates in three modes:

Mode Description
ingest_lists Bulk-load all active notices from the selected agencies into the dataset
screen_queries Fuzzy name-match a list of names against all notices; returns hits with match scores
new_listings_diff Compare the current notice set against the previous run's snapshot; output only newly published entries

Output fields

Each record includes:

Field Description
notice_id Canonical cross-agency ID (agency:source_id)
source_agency fbi, interpol, or europol
source_notice_id Agency-native notice ID
source_url Direct URL to the notice on the source site
notice_type Notice type: most_wanted, red, cyber, counterterrorism, etc.
primary_name Full name of the wanted individual
alias_names Known aliases (pipe-separated)
date_of_birth Date of birth (YYYY-MM-DD where available)
nationalities Nationalities (pipe-separated ISO codes or country names)
photographs Photo URLs (pipe-separated)
charges Criminal charges (pipe-separated)
case_summary Brief description of the case
reward_amount_usd Reward offered in USD (numeric)
last_known_location Last known location or jurisdiction
status active, captured, deceased, located, or removed
publication_date Date the notice was first published
query_term Query used (screen_queries mode only)
match_score Fuzzy match score 0-100 (screen_queries mode only)
match_reason Human-readable match explanation (screen_queries mode only)

See the full dataset schema for all 35 fields.

Input

{
  "mode": "ingest_lists",
  "agencies": ["interpol", "europol", "fbi"],
  "noticeTypes": ["red", "most_wanted"],
  "maxItems": 100,
  "includeRemoved": false,
  "sp_intended_usage": "KYC compliance screening",
  "sp_improvement_suggestions": "none"
}

Key parameters

Parameter Type Default Description
mode string ingest_lists Operating mode (see table above)
agencies array all Agencies to include: fbi, interpol, europol
noticeTypes array all Notice types to filter (e.g. red, most_wanted, cyber)
maxItems integer 10 Maximum records to return. 0 = no limit
includeRemoved boolean false Include captured or removed entries
minRewardUsd integer 0 Filter to listings with reward >= this amount
queries array — Names to fuzzy-match (screen_queries mode only)
minMatchScore integer 75 Minimum fuzzy match score 0-100 (screen_queries mode only)

Use cases

  • KYC / AML screening — check customer names against international wanted lists before onboarding
  • Compliance monitoring — run new_listings_diff on a schedule to detect newly posted notices
  • OSINT investigations — bulk-load public wanted data for research, linking, and analysis
  • Journalism — pull structured data for story research without manual browsing
  • Bounty tracking — filter by minRewardUsd to surface high-value notices

Notes

  • FBI data is fetched via Playwright with residential proxy to bypass Cloudflare protection on fbi.gov.
  • Interpol uses the public REST API with browser-like headers; residential proxy is used in cloud runs.
  • Europol scrapes structured HTML from the EU Most Wanted site.
  • All three sources serve publicly available, government-published data. No authentication or API key is required.
  • Array values (aliases, nationalities, photographs, charges) are stored as pipe-separated strings.
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