🏛️ GLEIF LEI Entity Resolution — Bulk Reference + Fuzzy Match

Stop paying Bloomberg, Refinitiv, or Dun & Bradstreet for entity reference data that already lives, for free, in the official GLEIF Level-1 + Level-2 datasets. This actor resolves a company name, address, or 20-character LEI to the canonical Global Legal Entity Identifier Foundation record — with parent + child corporate hierarchy, jurisdiction, registration status, fuzzy-match scoring, and the official source URL on every row.

The GLEIF system is the only globally-recognized, regulator-mandated entity identifier for financial counterparties (ISO 17442). If your KYC, OFAC screening, derivatives reporting, or counterparty risk workflow doesn't anchor on LEIs, you're flying blind. This actor turns the public GLEIF data feed into a clean, deterministic, hierarchy-aware lookup that drops directly into your operations pipeline.

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✅ What you get

Field	Description
LEI	The official 20-character ISO 17442 Legal Entity Identifier
legal_name	Registered legal name (in registration language)
legal_form	ELF code (e.g. H1UM = Inc., XTIQ = LTD, 8888 = unknown)
country	ISO 3166-1 alpha-2 country code from the legal address
address	{legal, headquarters} — flat string per address type
registration_status	Entity status (ACTIVE, INACTIVE, NULL)
registration_workflow_status	GLEIF workflow status (ISSUED, LAPSED, PENDING_VALIDATION, …)
parent_LEI	Direct parent LEI (GLEIF Level-2 relationship feed)
ultimate_parent_LEI	Ultimate corporate parent LEI
child_LEIs	Array of direct-children LEIs (capped at 200 per entity)
last_update_date	Most recent GLEIF reference-data refresh for this entity
initial_registration_date	When this LEI was first issued
next_renewal_date	When this LEI is next due for revalidation
jurisdiction	ISO 3166-2 jurisdiction code (e.g. US-CA, GB, LU)
managing_lou	The Local Operating Unit (LOU) that maintains the record
bic_codes	Linked SWIFT/BIC codes (for banks / financial institutions)
fuzzy_match_score	0..1 token-aware match score (only set for lookup_type='name')
source.api	Canonical GLEIF API URL for this LEI (audit trail)
source.gleif_search	Human-readable search.gleif.org record URL

Every row is one billable entity-resolved event.

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🎯 Use cases

Bank KYC / customer-onboarding ops

Resolve a new counterparty name to its canonical LEI in seconds, then traverse the corporate hierarchy to identify the ultimate beneficial parent — the standard first step of every CDD / EDD workflow under FATF, BSA, and AMLD6.

Derivatives back-office (EMIR / Dodd-Frank Title VII / MiFIR)

Every OTC swap reported to a Trade Repository must reference both counterparties' LEIs. Use this actor to bulk-resolve a name list (e.g. an end-of-day reconciliation file) and auto-flag entries where the LEI is LAPSED, has rotated, or has changed parent — all of which break TR matching.

Hedge-fund counterparty-risk monitoring

Pull the full child hierarchy of every prime broker, ISDA-counterparty, or fund administrator on your books. Schedule a weekly refresh and diff against your last snapshot to detect entity-group reorganizations before they hit your credit limits.

M&A due diligence / sell-side advisory

Map a target's full legal-entity tree — direct parent → ultimate parent → all direct-children → grandchildren — by chaining LEI lookups. The GLEIF Level-2 dataset is the only globally-consistent source for this; commercial alternatives (Bureau van Dijk Orbis, D&B Family Tree) charge five figures per seat per year for the same data.

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📥 Sample input

{
  "query": "Apple Inc",
  "lookup_type": "name",
  "max_results": 3,
  "include_hierarchy": true
}

You can also pass a 20-character LEI directly:

{
  "query": "HWUPKR0MPOU8FGXBT394",
  "lookup_type": "lei",
  "include_hierarchy": true
}

Or search by city / country / address fragment:

{
  "query": "Cupertino",
  "lookup_type": "address",
  "max_results": 25,
  "include_hierarchy": false
}

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📤 Sample output (one row)

{
  "LEI": "HWUPKR0MPOU8FGXBT394",
  "legal_name": "Apple Inc.",
  "legal_form": "H1UM",
  "country": "US",
  "address": {
    "legal": "C/O C T Corporation System, 330 N. Brand Blvd, Suite 700, Glendale, US-CA, 91203, US",
    "headquarters": "One Apple Park Way, Cupertino, US-CA, 95014, US"
  },
  "registration_status": "ACTIVE",
  "registration_workflow_status": "ISSUED",
  "parent_LEI": null,
  "ultimate_parent_LEI": null,
  "child_LEIs": [
    "2549008YU9EOMHFUA249",
    "549300ZRBV4SY8GTOY54",
    "529900PM64WH8AF1E917"
  ],
  "last_update_date": "2026-03-03T16:34:33Z",
  "initial_registration_date": "2012-06-06T15:53:00Z",
  "next_renewal_date": "2027-03-08T17:27:20Z",
  "jurisdiction": "US-CA",
  "category": "GENERAL",
  "managing_lou": "5493001KJTIIGC8Y1R12",
  "bic_codes": ["APLEUS66XXX"],
  "fuzzy_match_score": 0.9474,
  "source": {
    "api": "https://api.gleif.org/api/v1/lei-records/HWUPKR0MPOU8FGXBT394",
    "gleif_search": "https://search.gleif.org/#/record/HWUPKR0MPOU8FGXBT394"
  }
}

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🚀 How to use

Apify Python client

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("nexgendata/gleif-lei-entity-resolution").call(run_input={
    "query": "JP Morgan Chase",
    "lookup_type": "name",
    "max_results": 10,
    "include_hierarchy": True,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["LEI"], item["legal_name"], "->",
          "parent:", item["parent_LEI"],
          "children:", len(item["child_LEIs"]))

cURL

curl -X POST \
  "https://api.apify.com/v2/acts/nexgendata~gleif-lei-entity-resolution/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"query":"Goldman Sachs","lookup_type":"name","max_results":5,"include_hierarchy":true}'

MCP / agent integration

This actor exposes a deterministic schema and a single high-leverage tool (resolve_entity) — wire it into any MCP-compatible agent (Claude Desktop, Cursor, your in-house RAG stack) and your assistant can answer "who is the ultimate parent of Acme Bank's London branch?" without you handing it a Bloomberg session.

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⚖️ Legal & data source notes

• Data source: GLEIF Level-1 + Level-2 public API and the GLEIF Concatenated File. GLEIF reference data is published under CC0 1.0 Universal — no licensing fee, no per-record charge, no attribution requirement (though we surface the canonical source URL on every row regardless).
• Polite-bot identification: every request carries User-Agent: NexGenData GLEIF Actor hello@thenextgennexus.com. Concurrency is capped at 6 in-flight requests, well under GLEIF's documented soft-limit comfort threshold.
• No PII: GLEIF reference data is by design free of natural-person PII. The actor passes through legal-entity attributes only; no individual / director / beneficial-owner names are included.
• Hierarchy caveats: GLEIF Level-2 (parent-child) relationships rely on each entity self-reporting its parent. About 35% of LEIs currently have a documented parent-reporting exception (e.g. "no consolidating parent", "non-public information"). When that's the case, parent_LEI / ultimate_parent_LEI come back null — that's the GLEIF ground-truth, not a scrape failure.

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❓ FAQ

Q: How is this different from the free search.gleif.org website? A: search.gleif.org is the single-record human UI. This actor returns structured JSON, ranks fuzzy name matches, traverses parent + child hierarchy in one call, and integrates directly with your data pipeline / Excel / MCP agent. It's the GLEIF API, productized for ops.

Q: How fresh is the data? A: Real-time. Every request hits the live GLEIF API; the meta.goldenCopy.publishDate field on each response identifies the most recent GLEIF golden-copy snapshot (refreshed multiple times per day).

Q: What if my counterparty doesn't have a LEI yet? A: For US/EU regulated entities, almost every legal entity above shell-company thresholds is now LEI-issued (post-EMIR and Dodd-Frank Title VII rollout). For exotic jurisdictions or pre-issuance, the actor returns 0 rows and exits cleanly — no crash, no charge for the empty result beyond apify-actor-start.

Q: How accurate is the fuzzy name match score? A: The score is the maximum of (a) raw character-sequence similarity and (b) token-sort similarity, both computed by Python's stdlib difflib.SequenceMatcher. In practice, scores ≥ 0.85 are reliable matches; 0.70-0.85 should be reviewed; below 0.70 indicates the name is ambiguous and you should refine the query.

Q: Why is child_LEIs capped at 200 per entity? A: A handful of mega-holding-companies (JPMorgan, BlackRock, Berkshire) have thousands of direct children. The 200-cap keeps the payload predictable and the run cost bounded. For exhaustive sweep, set include_hierarchy: false and chain a follow-up run on the child LEIs you care about.

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🔗 Related actors

Reference-data + KYC cluster — every one of these is a public actor in the NexGenData fleet and pairs naturally with this LEI resolver:

• Company Enrichment Tool — once you have the LEI, enrich the company with employee count, tech stack, social profiles, and contact info.
• B2B Leads Finder — flip the workflow: start from a target persona / industry, get contacts, then resolve their parent entities to LEIs here for compliance.
• SEC Form 13F Tracker Pro — pair institutional-investor LEI resolution with 13F equity holdings for a complete counterparty-exposure view.

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🏷️ About NexGenData

NexGenData operates 260+ public Apify actors covering business reference data, financial filings, real estate, IP, lead generation, and MCP servers for AI agents. All actors are PAY-PER-EVENT priced, work directly with the Apify Console, n8n, Make, Zapier, and the MCP ecosystem. Questions or custom-data requests? Email hello@thenextgennexus.com.