Verifying businesses is a foundational process for onboarding customers, managing risk, and meeting regulatory obligations in industries like finance, compliance, and insurance. Yet, despite the growing availability of digital tools, business verification often remains stubbornly manual. This friction creates bottlenecks in onboarding workflows, increases costs, and introduces risk when critical information is missed or misinterpreted.

So, why is business verification still so manual—and what can be done to change that?

Business Data Is Complex and Fragmented

Unlike individuals—who generally have standardized identifiers like government-issued IDs or social security numbers—businesses vary widely in how they are structured and represented in data. A company might operate under a trade name that differs from its legal name, exist as part of a broader group, or have subsidiaries and joint ventures in multiple countries.

To complicate matters, information about a company’s identity, ownership, and operations is spread across a fragmented ecosystem:

• Government registries and corporate databases

• News and media sources

• Regulatory filings

• Licensing bodies

• Sanctions and watchlists

• Manually scanned documents (e.g., certificates, shareholder lists)

This fragmented data landscape means that verifying a business often requires checking multiple sources, interpreting conflicting information, and reconciling discrepancies—all tasks that still rely heavily on human judgment.

Jurisdictional Variability and Lack of Standardization

Each jurisdiction has its own rules about what company data must be disclosed, how it is maintained, and how it can be accessed. In some regions, registries are modern, open, and digitized. In others, data is locked in PDFs or behind paywalls, is only available upon request, or is simply incomplete.

For example:

• In the UK, Companies House provides free and structured data on business entities.

• In parts of the Middle East or Africa, company records may be sparse, inaccessible, or require manual outreach to obtain.

• In the U.S., ownership transparency varies state-by-state, and beneficial ownership was not required until the Corporate Transparency Act took effect in 2024.

This inconsistency means automated tools often fail to deliver complete or trustworthy results across geographies, forcing compliance and risk teams to intervene manually.

Ownership and Control Structures Are Often Opaque

Understanding who owns or controls a business is one of the most critical—and most challenging—parts of verification. Many entities are part of nested ownership chains, trusts, or offshore structures that obscure the identity of true beneficial owners.

Regulators such as FATF and national authorities require firms to identify and verify beneficial owners as part of AML/CFT obligations. However, data on ownership is frequently:

• Unavailable or self-declared

• Buried in unstructured filings

• Spread across shell companies in secrecy jurisdictions

Parsing through layers of documents, PDFs, and registries to trace ultimate ownership is a task that still heavily relies on human analysts, especially when automated tools can’t reliably extract or match this information.

Arva directly searches company registry data and can automatically investigate & map corporate ownership structures in over 140 jurisdictions, up to 15 layers deep.

Risk Assessment Requires Contextual Understanding

Verification isn’t just about confirming a business exists—it’s about understanding its risk profile. This involves:

• Screening for adverse media

• Identifying politically exposed persons (PEPs)

• Checking for sanctions or enforcement actions

• Assessing reputational or operational risk

These risk signals are often hidden in unstructured data—such as news articles, litigation databases, or social media—requiring careful interpretation to separate signal from noise. Many systems can surface raw data, but not all can interpret whether a news hit is relevant, material, or associated with the correct entity. As a result, compliance teams spend time manually triaging alerts, validating hits, and making judgment calls.

How Arva.ai Makes Business Verification Smarter and Less Manual

Arva.ai is designed to tackle these challenges head-on by combining deep data intelligence with powerful automation. It helps organizations streamline business review and risk analysis by providing:

✅ Unified Entity Profiles: Arva.ai brings together structured and unstructured data into a single, coherent view of a business—including registration details, ownership hierarchies, and associated entities.

✅ Automated Risk Extraction: Instead of dumping raw media hits or registry data, Arva.ai extracts and structures key risk insights—such as whether a business is linked to sanctions, fraud, or reputational issues—using AI models trained on real-world compliance use cases.

✅ Global Coverage: Arva taps into corporate data sources, news, and registries across jurisdictions, helping teams scale their processes without sacrificing depth or accuracy.

✅ Customizable Workflows and Overrides: Users can review, override, and annotate risk findings, enabling a mix of automation and expert oversight—critical for regulated workflows.

✅ Seamless API Integration: Whether you're onboarding one business or one thousand, Arva.ai plugs into existing systems to deliver instant verification and scoring at scale.

The Future Is (Less) Manual

Business verification doesn't have to be a slow, manual process. With the right technology, organizations can move from fragmented checks and analyst-heavy reviews to seamless, data-driven decisions. Arva.ai is helping power that transition—turning complex data into clear, actionable insights that teams can trust.

Learn more at Arva.ai.