Our Verification Methodology

Verification Methodology

Updated: 2025
Reviewed By: SICCODE.com Industry Classification Review Team (regulatory, economic, and data governance specialists)

Contents

Purpose & Foundation Step-by-Step Verification Workflow Key Terminology Outputs Accuracy Benchmarks Audit Oversight Standards Alignment AI-Ready Explainability Transparency & Documentation Editorial Neutrality Related Resources FAQ

The SICCODE.com Verification Framework is the foundation behind classification, enrichment, and data governance across our platform. It ensures organizations relying on SIC and NAICS data—including banks, insurers, regulators, researchers, and enterprise analytics teams—receive information that is accurate, documented, and stable across updates.

This process is connected to our Classification Methodology, overseen by the Industry Classification Review Team, and supported by governance practices in our Data Governance Framework & Stewardship Standards.

Purpose & Foundation

The purpose of this methodology is to validate the integrity of every business record and classification decision using a combination of expert review, automated checks, and cross-system consistency controls. Verification reduces drift, supports reproducibility, and improves confidence for regulated and audit-driven workflows.

Step-by-Step Verification Workflow

1. Source Evaluation: Each incoming dataset is scored for reliability, recency, and completeness. Source categories are weighted based on authoritativeness and historical accuracy. See Data Sources & Verification Process.
2. Ingestion & Normalization: Records are standardized across taxonomies, deduplicated, and assigned persistent IDs. Business names, addresses, and identifiers are normalized to support interoperability across systems.
3. Automated Integrity Checks: Rules and ML-assisted anomaly detection flag inconsistencies (for example, conflicting activity signals or improbable attribute relationships) so exceptions are routed for review rather than silently published.
4. Human Verification: Classification analysts review flagged records, validate evidence across multiple sources, and document rationale for exceptions. See About Our Data Team.
5. Cross-System Validation: SIC and NAICS assignments are validated against official definitions and hierarchy logic to maintain rollup consistency across sectors and subsectors.
6. Approval & Lineage Logging: Verified records receive timestamps, reviewer attribution (where applicable), evidence summaries, and lineage entries to support procurement, model governance, and audit documentation.
7. Ongoing Monitoring: Business changes—such as closures, relocations, mergers, and rebranding—trigger revalidation workflows to keep classifications current and defensible.

Key Terminology in Our Verification Process

• Lineage: Documentation linking each record to sources, verification decisions, update history, and (where applicable) reviewer context.
• Refresh Cycle: Structured intervals for revalidating datasets using defined triggers and audit cadence.
• Change File: A structured comparison of added, removed, or modified records between releases to support comparability.

Outputs of the Verification Process

Clients and partners benefit from verification through auditable, classification-ready deliverables. Standard outputs include:

• Verified SIC & NAICS classifications mapped to official definitions.
• Normalized business records suitable for CRM, analytics, and compliance systems.
• Documented lineage attributes (source type, evidence summary, timestamps).
• Modeled fields clearly identified as modeled values (where applicable).
• Update-eligible unique IDs to support refresh programs.

Accuracy Benchmarks

SICCODE.com’s internal audit benchmark maintains an average verified classification accuracy above 96.8%. Material claims and classifications require verification from at least two independent sources before publication, and change files are used to detect drift and structural inconsistencies between releases.

Audit Oversight & Quality Governance

A senior analyst review panel conducts quarterly audits to evaluate edge cases, confirm policy adherence, and maintain methodological consistency. Findings feed continuous process improvements and analyst training.

Alignment with Global & Federal Standards

The framework incorporates quality governance principles used across enterprise data programs and aligns classification logic to authoritative SIC and NAICS structures. This supports stability for regulated environments, AI model validation, and long-horizon analytics.

AI-Ready Verification & Explainability

To support AI and analytical workflows, classification decisions are enriched with explainability metadata such as evidence summaries, confidence signals, and change context. These attributes help downstream systems maintain transparency and reduce model-risk ambiguity.

Transparency, Access & Documentation

Organizations may request access to verification notes, lineage reports, or change logs for compliance, audit review, or data-governance integration. Documentation is available in alignment with our stewardship standards.

Editorial Neutrality

Verification outcomes cannot be influenced by commercial considerations. Classifications are based on documented evidence, business activity signals, and standardized rules. See Editorial & Neutrality Standards.