Verify a Company LEI for KYB Checks

The GLEIF LEI Scraper helps onboarding teams, procurement operations, and compliance analysts verify a supplied LEI against GLEIF. It collects public records into a bounded Apify dataset while preserving the query or category that produced each result. That provenance matters when a spreadsheet becomes a recurring workflow rather than a one-off browse.

This guide uses a deliberately narrow starting point: exact LEI HWUPKR0MPOU8FGXBT394. Small inputs make field coverage, duplicate behavior, source changes, and cost visible. A large first run can hide all four.

Build a four-way identity check

A practical KYB check compares four things: the identifier submitted by the counterparty, the legal name in the contract, the jurisdiction or registration context, and the official GLEIF record. Exact LEI lookup removes search ambiguity but does not guarantee those other fields agree. Route mismatches to a reviewer instead of automatically correcting the onboarding form.

Record entity status and registration status separately. They describe different parts of the record and should not be collapsed into one green or red label. Keep next-renewal and last-update dates so reviewers can see freshness. The final decision log should state what was checked, what matched, what did not, and which additional evidence was used. This makes the Actor useful in a controlled process without pretending that an API response completes KYB by itself.

Define the question before collecting data

Write down the decision the dataset is meant to support. Name the records that qualify, the freshness window, the minimum fields required, and who reviews exceptions. For this workflow, entity and registration status should be examined alongside stable identifiers and current source URLs. Avoid a score that quietly combines unrelated signals.

Set a maximum result count that is cheap to inspect by hand. Ten to fifty records is usually enough for the first pass. Open several ordinary rows, at least one sparse row, and one surprising result. If those examples do not support the intended question, adjust the input before scheduling anything.

Run a bounded Apify Task

Use the Actor input form to encode exact LEI HWUPKR0MPOU8FGXBT394. Keep each saved Task focused on one question, geography, topic, category, or counterparty set. Focused Tasks are easier to name, retry, audit, and retire.

After the run finishes, save the Actor build number, run ID, dataset ID, input, and collection time with the export. The Actor returns LEI, legal name, other names, jurisdiction, legal and headquarters addresses, entity status, registration status, managing LOU, and renewal dates. Preserve raw values. Put classifications, scores, and business rules in a separate reviewed transformation so source evidence is never overwritten.

Check the evidence at the source

Keep the submitted identifier, returned record, collection time, and reviewer decision as separate evidence. Sample more records after a source-layout or API change. Check identifiers, canonical URLs, dates, numeric fields, arrays, and null rates. A result should be reproducible from its input and source link.

An active LEI confirms a maintained identifier record, not ownership, solvency, sanctions clearance, or commercial legitimacy. That limitation belongs in the workflow documentation, not in a footnote added after someone questions the output. Missing fields should remain null rather than becoming zero, false, or an invented label.

Turn snapshots into reliable monitoring

Choose a cadence that matches the decision. Daily collection suits fast-moving operational queues. Weekly snapshots are often enough for market or catalog monitoring. Monthly runs can support slower benchmarks. Store the previous successful dataset and compare stable IDs plus named material fields.

Do not advance the baseline after a failed, partial, or unexpectedly empty run. Separate additions, updates, and removals. An empty dataset is an incident to investigate, not proof that the market disappeared. Alerts should include changed fields, collection time, the source URL, and a link to the Apify run.

Model the dataset without erasing history

Use the source identifier as the primary upsert key. Keep first-seen, last-seen, source-updated, and collected-at timestamps separate because they answer different questions. Retain the original text beside any normalized value. If entity resolution is needed, store the mapping with a confidence note and reviewer rather than silently merging names.

For trend analysis, compare like with like. The same queries, categories, page limits, sort order, and Actor version should be used across snapshots. If an input changes, begin a new series or annotate the break. Otherwise a collection change can be mistaken for a market change.

Export the result and control access

Apify datasets can be downloaded as JSON, CSV, or Excel or consumed through the API, webhooks, Make, Zapier, n8n, and MCP workflows. Keep credentials outside Actor inputs. Restrict downstream access to the use case and define retention for raw snapshots, derived tables, and alerts.

The GLEIF LEI Scraper on Apify charges per saved result and exposes an explicit result limit. Start with a small verified run. Scale only when the extra records change a real decision and the review process can absorb them.

Operational checklist

Before scheduling, confirm that the input is allowed, bounded, and documented. Verify representative output against the source. Record expected row count and acceptable null rates. Assign an owner for failed runs and source changes. Finally, write a stop condition: retire or revise the Task when its data no longer supports the original decision.

Assemble a compact KYB evidence packet

For an exact LEI, store the submitted identifier, returned legal name, entity status, registration status, legal address, jurisdiction, managing LOU, next renewal date, conformity flag, source URL, and collection timestamp. Compare these facts with the onboarding form and approved company-registry evidence. Differences should create a case with named fields rather than an automatic rejection. GLEIF confirms standardized identity records; it does not replace beneficial-ownership, sanctions, licensing, or fraud controls. Keep those decisions and evidence sources separate in the audit record.