Global Data Check

Global Data Check matches a person against the Global Data Universe and returns a per-field breakdown of how well the supplied details line up with one or more candidate records.

It is a single endpoint - POST /globaldata_check - that supports a wide range of identity verification, contact validation, and lookup workflows. The formal request and response schema (with every field, default, and enum) lives in the Global Data Check API reference; this guide focuses on how to use it and walks through worked sandbox examples for each common use case.

The check runs against the Global Data Universe, an in-house dataset of person and contact records aggregated from many sources.

The response is match information only - no PII is returned. Every field in match_results is a status flag (match, no_match, match_year, etc.) describing how the supplied input compares to a candidate. The candidate's underlying details (their actual DOB, phone, address, etc.) are never sent back. This makes the check a clean choice for confirmation flows where you already hold the data you are checking and just need a yes/no/partial signal.

The check is also very fast and is suitable for high-volume bulk processing - for example, scoring an entire customer database overnight or running real-time checks at signup throughput:

Per-request times vary with how much narrowing the supplied identifiers provide - a request with a phone, email, DOB, or full address resolves on the fast path; broad searches against very common last names with no other discriminator make up the slower tail.

Common things you can do with it:

• Confirm a person exists with a given name and date of birth (KYC / identity verification).
• Confirm that a phone number or email belongs to a particular person (signup flows, fraud checks).
• Verify someone lives at a given address, with full or partial address inputs.
• Find candidate matches for a partially-known identity to support manual review queues.
• Match against typos, nicknames, and reversed dates of birth without a separate fuzzy-matching pipeline.
• Score the match confidence and use it to drive auto-approve / manual review / reject routing.