Stop "Throwing Bodies" at the Problem: The Case for Automated Verification

When you're processing 5,000 loan applications per month, verification becomes a staffing problem. "Throwing bodies at the problem" is how one operations executive at a high-volume lender described their approach to Secretary of State lookups—hiring more people to manually navigate state websites, screenshot results, and upload documentation to their CRM. Another lending operations manager called it a "rote monotonous task" that consumed their team's best hours. The math is brutal: at 5-15 minutes per verification, 5,000 monthly applications translates to 175+ hours of manual labor—roughly one full-time employee doing nothing but copying and pasting state registration data.

This is the operational wall that separates lenders who scale from those who stall. The solution isn't more headcount. A business verification API replaces the manual workflow entirely, compressing hours of state website navigation into seconds of automated data retrieval. But understanding why automation works requires first understanding exactly where the manual process breaks down.

The Hidden Cost of the "Rote Monotonous Task"

Manual Secretary of State verification follows a predictable pattern: an underwriter receives an application, navigates to the appropriate state website, searches for the business entity, interprets the results, screenshots the page, uploads the screenshot to the loan file, and moves to the next application. Multiply that workflow by thousands.

According to a 2024 poll by Moody's Analytics, 56% of financial institutions reported that manual data collection and the ensuing back-and-forth with borrowers represents their biggest challenge in initiating the loan process.¹ This isn't surprising when you quantify the labor involved.

The Time Calculation

Consider a mid-sized alternative lender processing 5,000 applications monthly:

• Best case (5 minutes per verification): 416 hours/month • Average case (10 minutes per verification): 833 hours/month
• Complex cases (15+ minutes): 1,250+ hours/month

Even at the most optimistic estimate, you're burning over 400 hours monthly on a task that adds no analytical value. Your underwriters aren't assessing risk during this time—they're clicking through state websites and waiting for pages to load.

The Dollar Calculation

At $25/hour fully loaded labor cost (conservative for experienced underwriting staff), 175 hours of manual verification costs approximately $4,375 per month—over $52,000 annually. That's the cost of a junior underwriter whose entire job consists of navigating government websites.

But direct labor is only part of the equation. The FDIC's 2024 Small Business Lending Survey found that while 30% of banks can approve small loans within one business day, manual verification processes remain a key bottleneck preventing faster throughput.² Every day an application sits in verification is a day the borrower might close with a competitor.

The Opportunity Cost

High-volume lenders like Idea Financial, 1West Finance, and Bitty Advance process 5,000-10,000 applications monthly. At that scale, manual verification creates a ceiling on growth. You can't double your application volume without doubling your verification staff—and hiring takes months while applications arrive daily.

One operations leader described the dilemma bluntly: "When you're doing thousands and thousands of submissions a month, keeping those incompletes to a minimum becomes very important—either solve it by adding a lot of bodies to the problem or finding tech."

The Automation Solution: Human Workflow vs. API Workflow

The gap between manual and automated verification isn't incremental—it's categorical. Understanding the workflow differences explains why automation delivers such dramatic efficiency gains.

What does the manual process look like?

Step 1: Underwriter receives application with business name and state of incorporation

Step 2: Underwriter navigates to appropriate Secretary of State website (50 different sites, 50 different interfaces)

Step 3: Underwriter enters search parameters, handles CAPTCHA challenges, interprets results

Step 4: Underwriter identifies correct entity among potential matches (handling LLC/Inc variations, typos, name changes)

Step 5: Underwriter screenshots results, saves file with appropriate naming convention

Step 6: Underwriter uploads screenshot to loan file in CRM/LOS

Step 7: Underwriter logs verification details and moves to next application

Each step introduces latency and error potential. State websites vary wildly in design and reliability. Some states require registration to search. Others have CAPTCHA gates or session timeouts. Pennsylvania's system is notoriously slow. California's interface requires specific search syntax.

What does the API process look like?

Step 1: Application data triggers API call with business name and state

Step 2: API returns entity data with timestamped screenshot URL

That's it. Two steps versus seven. The API handles state website navigation, result interpretation, and documentation automatically. Every response includes the same structured data format regardless of which state you're querying.

A 2024 study published in Management Science found that algorithmic underwriting outperformed human underwriting processes, delivering 10.2% higher loan profits and 6.8% lower default rates.³ The performance difference was most pronounced for complex cases—exactly where human underwriters tend to slow down or make errors.

How does async architecture handle volume spikes?

When your marketing campaign drives a surge in applications, manual teams scramble. Overtime costs spike. Backlogs form. Application aging increases.

Automated systems handle volume spikes without degradation. Cobalt's API supports asynchronous processing patterns: submit verification requests, receive a retryId or provide a callbackUrl, and collect results as they complete. Your underwriting queue processes continuously without blocking on slow states.

For details on architecting your verification pipeline for high-volume periods, see our guide on handling high-volume application spikes.

[FLOWCHART: Manual Process vs. API Process]

Side-by-side comparison showing the manual workflow (7 steps with branching paths for different states, error handling, and re-verification) versus the API workflow (2 steps with parallel processing capability). Include time estimates at each step: Manual total 5-15 minutes, API total 1-3 seconds for cached results, 7 seconds to 2 minutes for live queries. Highlight the "screenshot and upload" manual step that the API eliminates entirely by generating timestamped screenshots automatically.

Moving from "Data Entry" to "Exception Handling"

Automation doesn't eliminate human judgment—it redirects it. The goal isn't to remove underwriters from the verification process entirely. The goal is to ensure underwriters spend their time on decisions that require expertise rather than tasks that require clicking.

What should humans handle?

The 2% that requires judgment:

• Applications with multiple potential entity matches requiring manual disambiguation • Businesses with complex corporate structures spanning multiple states • Edge cases where SOS data conflicts with other application information • High-value transactions warranting additional due diligence • Flagged results requiring compliance review

What should automation handle?

The 98% that requires execution:

• Standard entity lookups across all 50 states • Screenshot generation and documentation • Data normalization (handling LLC/Inc/Corp variations) • Fuzzy matching with confidence scoring • Result caching for repeat queries • Audit trail maintenance

This division of labor transforms underwriting from data gathering to decision making. Your most experienced staff focus on the applications that benefit from their expertise while automation handles the volume.

The global underwriting software market reached $5.7 billion in 2023 and is projected to grow to $15.9 billion by 2032—a 12.5% compound annual growth rate—driven precisely by this shift toward automation handling routine tasks while humans handle exceptions.⁴

What does the transition look like operationally?

Chicago-based lender Lendr provides a case study in automation's impact. Before implementing document automation, underwriters spent over five hours weekly on manual bank statement verification alone, contributing to loan approval times of 30-45 days. After automation, processing time dropped to 12 minutes—saving 70,000 hours annually and cutting costs by $560,000.⁵

More importantly, Lendr's team shifted focus to higher-value activities: improving customer service, refining underwriting criteria, and growing loan volume without adding headcount.

From Bottleneck to Competitive Advantage

The lending industry is in the midst of a technological transition. The Baker Tilly analysis of RPA in financial institutions notes that automation is transforming operations across loan origination, processing, underwriting, funding, and monitoring.⁶ Lenders who automate verification gain speed advantages that compound across every other stage of the loan lifecycle.

Consider speed-to-funding as a competitive differentiator. A borrower applying to multiple lenders simultaneously will often close with whoever funds first. If your verification process takes days while competitors verify in seconds, you're systematically losing deals to faster operators—regardless of how competitive your rates might be.

The operational benefits cascade:

• Faster verification → faster underwriting → faster funding → higher close rates • Lower verification cost → lower cost per funded loan → competitive pricing flexibility • Consistent documentation → cleaner audit trails → reduced compliance risk • Scalable processing → growth without proportional headcount → improved unit economics

The alternative lending market continues expanding rapidly. Lenders still "throwing bodies at the problem" will find themselves increasingly unable to compete with automated operators who can process more volume at lower cost with better accuracy.

Beyond Speed: Ensuring Data Accuracy

Automating verification solves the speed problem. But speed without accuracy creates new risks—you're simply making bad decisions faster. The next challenge is ensuring automated systems return data you can trust.

Manual verification introduces human variability: different underwriters interpret the same state website differently, handle edge cases inconsistently, and occasionally make transcription errors when documenting results. Automation eliminates these inconsistencies but introduces new considerations around data freshness, matching accuracy, and source reliability.

For a detailed analysis of data quality challenges in automated verification—including why cached database results create hidden risks and how live lookups prevent "stale data" errors—see our guide on ensuring data accuracy in automated flows.

See How Cobalt Automates This

Your underwriting team is either gathering data or making decisions. Automation shifts the balance toward decision-making—the work that actually requires human expertise.

Cobalt's business verification API handles the data gathering: all 50 states plus D.C., timestamped screenshots for every query, async processing for high-volume pipelines, and consistent structured responses regardless of state. Your team handles the exceptions and decisions that benefit from their experience.

See how Cobalt automates this →