The Lender's Guide to Automating KYB with an SOS API
Introduction
For alternative lenders processing hundreds or thousands of applications monthly, manual Know Your Business (KYB) verification has become the "Achilles heel" of scaling operations. Every loan application requires confirming that the borrowing entity actually exists, operates in good standing, and lists legitimate officers—a process that traditionally consumes 20 to 30 minutes per file when done by hand. Multiply that across a growing pipeline, and you've created a bottleneck that no amount of hiring can sustainably solve.
The solution lies in automation through an SOS API for lenders that connects directly to Secretary of State databases across all 50 states. Rather than dispatching underwriters to navigate state portals one by one, API-driven KYB verification retrieves entity status, formation dates, officer names, and registered agent information in seconds. FinCEN estimated that implementing comprehensive KYB compliance practices would cost financial institutions between $1.5 billion and $10 billion collectively¹—but those costs drop dramatically when verification workflows are automated rather than staffed.
This guide walks lenders through what modern KYB entails, why surface-level "good standing" checks fall short of genuine due diligence, and how API automation transforms KYB from a manual burden into a competitive advantage.
What is KYB in Modern Lending?
Know Your Business (KYB) is the regulatory due diligence procedure designed to confirm a business entity's legal status, understand its operational activities, and assess its exposure to money laundering and related financial crime risks². While Know Your Customer (KYC) focuses on individual identity verification, KYB examines the corporate structure, ownership, and legitimacy of business borrowers.
For lenders, KYB verification encompasses several interconnected checks:
• Entity verification: Confirming the business is legally registered with the appropriate Secretary of State and currently authorized to transact business • Beneficial ownership identification: Determining the Ultimate Beneficial Owners (UBOs) who own 25% or more of equity interests or exercise substantial control³ • Officer validation: Verifying that individuals listed on loan applications actually hold the positions they claim within the company • Compliance screening: Running the business and its principals against sanctions lists, PEP databases, and adverse media sources
Credit providers must collect and verify core business information including legal business name, trade names or DBAs, physical business address, registration documents, and tax identification numbers³. The verification process extends beyond accepting documents at face value—lenders should cross-reference information against multiple authoritative sources, including government business registries and corporate databases, to identify discrepancies that might indicate fraud.
The compliance burden has intensified significantly. The Corporate Transparency Act's beneficial ownership information reporting requirements went into effect January 1, 2024, requiring certain entities to report personally identifiable information about beneficial owners directly to FinCEN⁴. While this creates a federal database, lenders still need independent verification that the businesses seeking financing actually match their claimed identities and statuses.
Why "Good Standing" Isn't Enough
A common mistake among lenders is treating "Active" or "Good Standing" status as sufficient KYB verification. While entity status matters—you certainly don't want to fund a dissolved or administratively suspended company—it represents only the first layer of genuine due diligence.
Consider what "Good Standing" actually confirms: the entity filed its annual reports and paid its franchise taxes. It says nothing about whether the company has been in business for five years (as claimed on the application) or five days. It doesn't verify that "John Smith, CEO" actually appears in the Secretary of State records as an officer. And it provides no insight into whether the registered agent address is a legitimate business location or a virtual mailbox associated with dozens of shell entities.
The data points that expose fraud and misrepresentation often lie deeper:
• Formation date: When was the entity actually created? A business claiming five years of operating history but showing a formation date from last month warrants immediate scrutiny. Understanding verifying business registration dates helps lenders catch aged shelf companies purchased specifically for fraudulent loan applications.
• Officer data: Do the officers listed on your loan application match the officers on file with the Secretary of State? Misalignment here—a "CEO" who isn't listed as an officer, or officers who don't match the application signatories—signals either sloppiness or deliberate misrepresentation.
• Filing history: Has the entity maintained continuous good standing, or does the record show administrative dissolutions followed by reinstatements? A pattern of lapses suggests operational instability or negligent management.
• Registered agent changes: Frequent registered agent changes, particularly to addresses associated with incorporation mills, can indicate shell company activity.
The pandemic relief fraud epidemic demonstrated exactly what happens when lenders rely on surface-level verification. The SBA Office of Inspector General estimated that 8% of total disbursed PPP funds—approximately $64 billion—were fraudulently obtained⁵. As of December 2023, U.S. Attorneys' Offices had criminally charged approximately 3,500 defendants in 2,388 pandemic fraud cases, with fraud losses exceeding $1.2 billion in completed cases alone⁶. Common fraud patterns included creating fictitious businesses (53% of prosecuted cases) and submitting multiple fraudulent applications using different business names (86% of cases)⁷.
Proper KYB verification that examined formation dates, officer records, and filing histories would have flagged many of these fraudulent applications before funding.
The Role of Officer Verification in Fraud Prevention
Officer verification deserves particular attention because it represents one of the most exploitable gaps in manual KYB workflows. When underwriters "screenshot and upload to Salesforce" from a state portal, they're capturing a moment-in-time view that may not align with application data or that may become stale before funding occurs.
Sophisticated fraud schemes rely on this verification gap. A fraudster might list themselves as CEO on a loan application for a company where they hold no official position. Without automated officer matching, this discrepancy might slip through—especially when underwriters are processing high volumes under time pressure.
The risk calculus here is straightforward. Approving a loan to someone who doesn't actually control the borrowing entity creates both fraud exposure and collection problems. If the actual company principals later dispute the debt, or if the entity was used without authorization, the lender faces losses that proper verification would have prevented.
Officer data is publicly available in 30+ states where Secretary of State offices publish this information. An API that retrieves and normalizes this data across jurisdictions enables automated matching between application signatories and official corporate records—transforming officer verification from a manual spot-check into a systematic control.
Automating KYB Checks
The case for automation becomes compelling when you examine both the efficiency gains and the accuracy improvements available through API-driven verification.
On efficiency: A poll by Moody's Analytics found that 56% of financial institutions reported manual data collection and the ensuing back-and-forth with borrowers represents their biggest challenge in initiating the loan process⁸. API-based KYC and KYB platforms accelerate customer onboarding by an average of 30%⁹, while banks using AI-enhanced underwriting report 50-75% reductions in time-to-decision for commercial loans¹⁰.
On accuracy: A randomized experiment in lending found that algorithmic underwriting outperforms human underwriting, resulting in 10.2% higher loan profits and 6.8% lower default rates¹¹. Studies comparing AI and human underwriting performance show AI systems typically achieve 15-30% higher accuracy in predicting defaults and delinquencies¹⁰.
An automated KYB workflow typically operates as follows:
Step 1: Application Intake When a business submits a loan application, the system captures the legal business name, state of formation, and officer/signatory information.
Step 2: API Query The Secretary of State API searches the relevant state database, returning entity status, formation date, officers, registered agent, and filing history in a normalized JSON format.
Step 3: Automated Matching The system compares application data against API results. Does the claimed business name match? Does the formation date support the claimed time in business? Do the application signatories appear as officers?
Step 4: Risk Flagging Discrepancies trigger automated flags for review. An "Inactive" status generates an auto-reject. A formation date inconsistent with claimed operating history routes to fraud review. Officer mismatches prompt additional verification.
Step 5: Evidence Capture The API generates timestamped screenshots of the Secretary of State records, providing audit-ready documentation without manual effort.
This automated workflow transforms KYB from a 20-30 minute manual task into a 3-7 second API call—while actually improving verification quality through consistent application of matching rules and comprehensive data capture.
Implementation Considerations for Fintechs and Lenders
Successful KYB automation requires more than simply integrating an API. Lenders and fintechs should address several implementation considerations:
Coverage breadth: Many verification providers offer partial coverage—perhaps 40 states, or coverage that excludes certain entity types. For lenders operating nationally, gaps in coverage mean maintaining parallel manual processes for uncovered jurisdictions. A solution covering all 50 states plus D.C. eliminates these workarounds.
Data freshness: The difference between cached data and live lookups matters significantly. Cached databases can be 30-90 days stale, missing recent status changes, dissolutions, or officer updates. Live queries against Secretary of State systems ensure you're verifying against current records, not outdated snapshots.
Response normalization: Each state structures its data differently. California's bizfile system returns different fields than Louisiana's geauxBIZ portal or Iowa's Business Entity Data API. A well-designed API normalizes these variations into a consistent response format, enabling uniform processing regardless of source state.
Audit documentation: Compliance programs require evidence that verification occurred. API-generated screenshots with timestamps and source URLs provide this documentation automatically, eliminating the manual "screenshot and upload" process that creates inconsistency and audit gaps.
Ongoing monitoring: KYB obligations don't end after onboarding. Credit providers must monitor business customer activity and update customer information on a risk-adjusted basis³. API solutions that support periodic re-verification enable systematic monitoring without manual portfolio reviews.
The ROI of KYB Automation
For operations leaders evaluating KYB automation, the return on investment calculation is straightforward.
Consider a lender processing 500 applications monthly with an average manual KYB verification time of 25 minutes. That's 208 hours of verification labor per month. At a fully-loaded cost of $25/hour, manual KYB costs approximately $5,200 monthly in labor alone—before accounting for errors, rework, and fraud losses from inadequate verification.
Automated KYB reduces per-application verification time to seconds, eliminates transcription errors, and improves fraud detection through systematic matching rules. The labor redeployed from manual verification can focus on exception handling, relationship management, and growth initiatives.
Beyond direct cost savings, automation enables scalability. Manual workflows impose a ceiling on application volume—you can only process as many applications as your verification staff can handle. API-driven verification removes this constraint, allowing loan volume to grow without proportional headcount increases.
[DIAGRAM: KYB Automation Workflow - Visual showing application intake flowing through API query, automated matching, risk flagging, and evidence capture, with manual review branching only for flagged exceptions]
[TABLE: Manual vs. Automated KYB Comparison
Conclusion
KYB verification represents both a regulatory obligation and a risk management imperative for lenders. The question isn't whether to perform thorough business verification—it's whether to do it manually or through automation.
Manual KYB processes worked when application volumes were lower and fraud schemes less sophisticated. Today's lending environment demands verification that is simultaneously faster, more accurate, and more comprehensive than what human processors can consistently deliver at scale.
The path forward involves embracing API-driven automation that queries Secretary of State databases in real-time, matches application data against official records systematically, and generates audit-ready documentation automatically. Lenders who make this transition gain both operational efficiency and improved risk management.
For those ready to move beyond KYB as a bottleneck, the next consideration involves reducing risk with speed—understanding how to maintain rigorous verification while meeting borrower expectations for rapid decisions.
See how Cobalt automates KYB verification → Get API Keys (30 Free Credits)
Sources
• Middesk | Know Your Business (KYB) identity verification explained
• Sumsub | KYB (Know Your Business) Verification Guide 2026
• LoanPro | Understanding Know Your Business (KYB) requirements and compliance for credit providers
• FinCEN | Beneficial Ownership Information Reporting FAQs
• TAF | PPP Fraud: Big Numbers, Bigger Numbers to Go
• LoanPro | From Manual to Modern: Expanding Your Market Potential Through Automated Loan Underwriting
• CoinLaw | API in Financial Services Statistics 2025
• V7 Labs | AI Commercial Loan Underwriting: Enhancing Credit Decisions
• Management Science | Rise of the Machines: The Impact of Automated Underwriting
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