What can databases actually prove?

They confirm attributes such as existence, identity fields, and contactability. They do not replace document authenticity or ownership checks, so combine with documents and liveness for higher assurance.

How are matches scored reliably?

Weighted attributes (DOB above address), normalization, and fuzzy logic. Emit scores and reasons so reviewers understand outcomes and route gray zones to manual queues with clear SLAs.

How do we address gaps and blind spots?

Maintain a matrix of sources by geography and product, define fallbacks, log coverage, and escalate low-confidence results to supplemental documents or human review.

What are the core compliance considerations?

Obtain consent, respect purpose limits, encrypt data, retain minimally, and keep provider versions and timestamps so examiners can trace decisions.

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Database Verification

Overview

Database verification corroborates identity or business details against authoritative or trusted sources such as government registries, credit bureaus, telecom data, professional licenses, postal databases, and consortium datasets. In KYC and KYB it confirms legal existence, name, date of birth, address, contactability, and risk indicators at speed and scale. Strong programs layer multiple sources to offset gaps, normalize names and addresses, and apply weighted matching with reason codes. Privacy-by-design is essential, including permissible purpose, consent capture, encryption, and minimal retention.

Implement result versioning, timestamps, and provider identifiers for auditability, and set TTL windows to avoid stale decisions. Database hits complement documentary and biometric checks, raising assurance and reducing manual effort. When signals conflict, tie-break rules and exception playbooks guide resolution. Continuous monitoring resyncs records after material changes like ownership or licensing status.

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