Enrich Counterparty Records with LEI Data

The GLEIF LEI Scraper helps data teams, procurement analysts, and financial institutions enrich an existing counterparty dataset. 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: active entities matched by legal name and country. Small inputs make field coverage, duplicate behavior, source changes, and cost visible. A large first run can hide all four.

Enrich records without overwriting supplier input

Add new fields such as matched LEI, official legal name, official address, entity status, match method, confidence, and reviewed-at date. Never replace the supplier's submitted name or address. Those raw values explain the match and may be needed to resolve a later dispute. Exact LEI matches can follow a different review path from fuzzy name candidates.

Measure enrichment quality on a labeled sample before scaling. Count correct matches, wrong matches, unresolved records, and cases where several candidates are plausible. Country filters can reduce noise but may exclude multinational contracting entities. Set an automatic-match threshold only after the false-positive rate is acceptable for the use case. Everything below that threshold belongs in a review queue with direct GLEIF links.

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, LEI, address, and renewal evidence 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 active entities matched by legal name and country. 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

Write enrichments to new columns, keep raw input untouched, and send uncertain matches to a human queue. 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.

Automated name matching can create false positives, especially for common names and international corporate groups. 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.

Measure match quality with an adjudicated sample

Select records across countries, name lengths, legal forms, and expected difficulty. Have reviewers label the correct LEI or unresolved outcome before tuning automated rules. Report precision separately for exact identifiers, normalized legal names, and candidate-name matches. A rule that performs well on large public companies may fail on local subsidiaries or funds with reused words. Preserve rejected candidates and reviewer reasons so the next iteration learns from actual errors. Scale enrichment only when the accepted false-match rate fits the downstream risk.