TL;DR: KYB (Know Your Business) verifies a company's legal existence, ownership structure, and risk profile before you onboard them. Most teams still do it manually, which means errors, delays, and compliance gaps that regulators love to target. This guide breaks down what KYB actually involves, why the manual approach fails, and how to build automated verification flows that scale. TypeScript examples included.
Your compliance team just spent three days verifying a shell company that dissolved six months ago. The manual KYB process missed the dissolution, the beneficial ownership trail led nowhere, and now you are explaining to regulators why due diligence failed.
If that sounds familiar, you are not alone. Most compliance teams treat KYB as a tick-box exercise, running basic company searches and calling it complete. Then they wonder why sophisticated money launderers sail through their onboarding process whilst legitimate businesses get stuck in verification limbo.
I am Stuart Watkins, CEO of Zenoo. We build compliance orchestration infrastructure, and we see variations of this story every week. So here is a KYB guide written for the people who actually have to build and maintain these systems.
KYB is not KYC for companies. It is harder.
KYC verifies an individual. One person, one identity document, one screening check. KYB verifies a business, which means confirming legal existence, ownership structure, ultimate beneficial owners (UBOs), licences, and screening against sanctions lists, blacklists, and grey lists for involvement in money laundering or terrorist financing.
The EU 5th AML Directive mandates KYB for 10 entity types: credit institutions, estate agents, external accountants, financial institutions, gambling services, notaries, service auditors, tax advisors, trusts, and investment firms.
But the real complexity is structural. A single business might have multi-layer ownership, subsidiaries across jurisdictions, and UBOs hidden behind nominee directors. Tracing ownership through those layers requires pulling data from registry sources like Secretary of State (SOS) databases and EIN/TIN records, then cross-referencing globally.
Here is a minimal type definition for what a KYB verification request actually looks like:
interface UltimateBeneficialOwner {
fullName: string;
dateOfBirth: string;
nationality: string;
ownershipPercentage: number;
isPoliticallyExposed: boolean;
}
interface KYBVerificationRequest {
companyName: string;
registrationNumber: string;
jurisdiction: string;
registeredAddress: string;
incorporationDate: string;
ubos: UltimateBeneficialOwner[];
sourceOfFunds: string;
transactionPurpose: string;
riskSector: 'standard' | 'elevated' | 'high'; // e.g., gambling, money services, adult entertainment
}
interface KYBVerificationResult {
companyExists: boolean;
registrationActive: boolean;
uboScreeningResults: {
uboName: string;
sanctionsHit: boolean;
pepHit: boolean;
adverseMediaHit: boolean;
}[];
riskScore: number; // 0 to 100
recommendedAction: 'auto_approve' | 'manual_review' | 'reject';
dataQuality: {
complete: boolean;
accurate: boolean;
fresh: boolean;
consistent: boolean;
unique: boolean;
};
}
That dataQuality object is not decoration. High-quality KYB datasets must meet five criteria: complete, accurate, fresh, consistent, and unique. Poor data quality leads to unreliable verification checks and increased false positives, which is already the top pain point compliance teams report across the industry.
Why manual KYB fails at scale
Manual processes cause errors and delays in business onboarding. That is not opinion. It is the pattern we see repeated across every compliance team still running spreadsheet-driven workflows.
Three specific failure modes:
1. Stale data kills due diligence. Traditional KYB stops at initial onboarding, missing post-approval changes. A company can go bankrupt, get added to a watchlist, or change its ownership structure entirely, and your records still show the snapshot from day one. The 2026 trend is towards continuous re-KYB: monitoring for status changes like bankruptcy, liens, watchlist additions, or registration updates in real time.
2. Complex structures exploit manual gaps. Shell companies exist specifically to exploit the complexity of multi-layer ownership. When an analyst is manually tracing UBOs through four jurisdictions, mistakes are inevitable. Sophisticated verification tools exist precisely because human pattern-matching breaks down at this scale.
3. Siloed processes obscure the full picture. Most teams run KYB, KYC, and AML screening as separate workflows. The 2026 push is towards integrated KYB-KYC-AML workflows that build complete risk profiles. Siloed processes create gaps that obscure full business risk assessment, and those gaps are exactly what regulators look for in enforcement reviews.
A Head of Compliance at a UK payments company put it to us plainly: "We had three separate teams running three separate checks on the same entity. None of them could see each other's findings. When the FCA asked us to reconstruct our due diligence on a flagged merchant, it took us two weeks to piece it together from different systems."
Building an automated KYB flow
Automation dominates 2026 KYB trends for good reason: manual verification processes systematically fail at scale, creating exactly the compliance gaps that regulators target in enforcement actions.
Here is what an API-driven KYB orchestration flow looks like in practice:
interface KYBOrchestrationStep {
provider: string;
checkType: 'registry_lookup' | 'ubo_extraction' | 'sanctions_screening' | 'pep_check' | 'adverse_media';
timeout: number; // milliseconds
required: boolean;
}
interface KYBOrchestrationConfig {
steps: KYBOrchestrationStep[];
riskPolicy: {
autoApproveBelow: number;
escalateAbove: number;
rejectAbove: number;
};
continuousMonitoring: boolean;
reKybTriggers: ('bankruptcy' | 'watchlist_addition' | 'ownership_change' | 'registration_lapse')[];
}
const defaultConfig: KYBOrchestrationConfig = {
steps: [
{ provider: 'registry_api', checkType: 'registry_lookup', timeout: 5000, required: true },
{ provider: 'ubo_service', checkType: 'ubo_extraction', timeout: 10000, required: true },
{ provider: 'sanctions_provider', checkType: 'sanctions_screening', timeout: 3000, required: true },
{ provider: 'pep_provider', checkType: 'pep_check', timeout: 3000, required: true },
{ provider: 'media_service', checkType: 'adverse_media', timeout: 8000, required: false },
],
riskPolicy: {
autoApproveBelow: 25,
escalateAbove: 60,
rejectAbove: 85,
},
continuousMonitoring: true,
reKybTriggers: ['bankruptcy', 'watchlist_addition', 'ownership_change', 'registration_lapse'],
};
The key design decisions here:
Rule-based policies enable automatic approvals and escalations based on risk thresholds. The riskPolicy config means low-risk businesses get approved without human intervention, medium-risk cases get escalated, and high-risk applications get rejected automatically. Your analysts spend their time on the cases that actually need human judgement, not rubber-stamping clean applications.
Continuous monitoring is not optional any more. The reKybTriggers array defines which post-onboarding events should trigger a re-verification. Real-time monitoring captures critical risk events after business relationships begin, which is the entire point of moving beyond one-time verification.
Steps run with timeouts and required flags. Adverse media might be useful but not blocking. Sanctions screening is always required. This lets you parallelise checks where possible and degrade gracefully when a non-critical provider is slow.
What good data quality actually looks like
Smart algorithms are being developed specifically to reduce false positive rates, but they are only as good as the data they ingest. Here is how to validate KYB data quality programmatically:
interface DataQualityCheck {
dimension: 'complete' | 'accurate' | 'fresh' | 'consistent' | 'unique';
check: (data: KYBVerificationRequest) => boolean;
failureMessage: string;
}
const qualityChecks: DataQualityCheck[] = [
{
dimension: 'complete',
check: (data) => !!(data.companyName && data.registrationNumber && data.jurisdiction && data.ubos.length > 0),
failureMessage: 'Missing required fields: company name, registration number, jurisdiction, or UBOs',
},
{
dimension: 'fresh',
check: (data) => {
const incorporationDate = new Date(data.incorporationDate);
return !isNaN(incorporationDate.getTime());
},
failureMessage: 'Incorporation date is invalid or missing',
},
{
dimension: 'unique',
check: (data) => {
const uboNames = data.ubos.map((u) => u.fullName.toLowerCase());
return new Set(uboNames).size === uboNames.length;
},
failureMessage: 'Duplicate UBO entries detected',
},
];
Incomplete, inaccurate, outdated, inconsistent, or duplicate data does not just slow down verification. It produces false positives, which means your compliance team wastes hours reviewing entities that are perfectly clean, whilst genuinely risky businesses slip through because analysts are overwhelmed.
Where most teams get stuck
Based on market intelligence from 211 data points across compliance vendors, here is what the industry actually scores:
API quality: 4.1/5. Compliance depth: 4.3/5. Integration ease: 3.9/5. Documentation: 3.5/5. Pricing value: 3.4/5.
The top pain points reported: false positives, complexity, security concerns, difficult photo capture instructions, and delayed response time.
Notice the gap between compliance depth (4.3/5) and documentation (3.5/5). The tools can do the job. The problem is that integrating them into a coherent workflow, one that covers KYB, KYC, and AML in a single pass, remains painful. API integrations enable data sharing between compliance functions, but most teams end up building custom glue code between three or four different providers.
This is exactly the integration gap that Zenoo was built to close. An API-first orchestration layer that connects your registry lookups, UBO extraction, sanctions screening, and risk scoring into a single configurable workflow, so you are not maintaining bespoke integrations for every provider combination.
The 30-second version
KYB verifies a business's legitimacy, ownership structure, legal existence, and risk profile. It is mandated by regulation, complicated by corporate structures, and broken by manual processes.
The fixes are not theoretical. Dynamic risk scoring replaces static compliance checklists. Continuous monitoring replaces one-time verification. Unified workflows replace siloed KYB, KYC, and AML processes. And API-first orchestration replaces months of custom integration work.
If you are building compliance flows and want to see what orchestrated KYB looks like with your own data, check out zenoo.com.
Stuart Watkins is CEO of Zenoo, the compliance orchestration platform. He has spent the last decade building infrastructure that makes compliance teams faster without making regulators nervous.
