How SBA 7(a) Loan Count Drops 27% and Pushes SMBs Toward MCA Underwriting Best Practices

The SBA Pullback Is Sending Thousands of Merchants to Your Door

The numbers are stark. SBA 7(a) loan counts fell over 27% in the most recent fiscal year, volume declined roughly 12%, and the average loan size jumped 21%. Lendesca CEO Chris Hurn told deBanked that the rising average size is a direct consequence of fewer small loans being approved, not a sign of program health. Fewer loans at larger ticket sizes means the SBA pipeline is consolidating around bigger, more bankable borrowers. Everyone else is looking for alternatives.

For MCA funders and ISO brokers, this is not abstract macroeconomic data. It is a direct pipeline event. The merchants who used to qualify for a $75,000 SBA loan and got rejected are now searching for working capital elsewhere. Many will land on merchant cash advance products. The question is whether your underwriting operation is ready to evaluate them properly, because MCA underwriting best practices for a typical cash advance applicant do not map cleanly onto a displaced SBA borrower.

This article breaks down why the SBA-to-MCA overflow demands a different underwriting lens, how automated bank statement analysis fits into that picture, and what funders should adjust before portfolio quality starts slipping.

Why Displaced SBA Borrowers Are Not Typical MCA Applicants

The Profile Mismatch

A merchant who applied for an SBA 7(a) loan and got declined is fundamentally different from a merchant who never considered bank financing in the first place. Traditional MCA applicants often have thin credit files, limited banking relationships, or business models that banks simply do not serve well. They know the MCA product, they understand the cost of capital, and their cash flow patterns tend to be volatile by nature.

Displaced SBA applicants, on the other hand, typically have more established businesses, longer operating histories, and expectations shaped by conventional lending. They may have been declined for documentation gaps, collateral shortfalls, or SBA-specific policy changes rather than fundamental credit weakness. That distinction matters because it changes how you should read their bank statements.

A merchant with six years of operating history and $90,000 in monthly revenue who got rejected by the SBA is not the same underwriting problem as a two-year-old business with the same revenue. The first merchant likely has more stable deposit patterns, fewer NSFs, and lower daily balance volatility. But they also may carry existing debt obligations that the SBA flagged, or seasonal patterns that SBA underwriters deemed too risky. If your underwriting team treats both merchants identically, you are either overpricing the displaced borrower or, worse, missing the specific risk factor that caused the SBA decline.

Volume Pressure Versus Accuracy

The 27% decline in SBA loan counts translates to tens of thousands of small businesses entering the alternative funding market each year. That volume does not distribute evenly across the calendar. It arrives in waves, often concentrated after SBA fiscal year-end reporting and during Q1 budget resets. Funders who rely on manual bank statement review simply cannot absorb these surges without either slowing down approvals or cutting corners on verification.

This is where the structural connection between SBA declines and MCA underwriting rigor becomes operational. Every additional day a deal sits in document collection is a day the merchant might accept funding from a competitor. Every manual review error on a bank statement is a potential default that erodes your portfolio. The funders who win this displaced volume will be the ones who can verify bank statements quickly, accurately, and at scale.

Automation Is No Longer Optional

Automated bank statement analysis has shifted from a nice-to-have efficiency tool to a structural requirement for any funder expecting to handle the SBA overflow. The reason is straightforward: displaced SBA borrowers often submit cleaner, more complete documentation than typical MCA applicants. Their bank statements tend to cover longer periods, include more transaction volume, and reflect more complex cash flow patterns with payroll, vendor payments, and loan servicing all visible in the data.

Parsing that complexity manually is slow and error-prone. AI-powered document extraction can categorize transactions, flag recurring obligations, identify average daily balances, and detect anomalies in minutes rather than hours. More importantly, it can do this consistently across hundreds of applications without fatigue-driven mistakes. When a funder is processing fifty displaced SBA applicants in a week alongside their normal pipeline, consistency is what prevents a bad deal from slipping through.

Let's Submit fits into this workflow by handling the upstream bottleneck. Before your underwriting team even touches a file, Let's Submit collects bank statements, government IDs, void cheques, and signed applications through a single asynchronous upload link. Merchants complete the process from their phones in minutes. AI extraction pulls revenue, deposits, and key fields automatically, so your team reviews clean, structured data instead of raw PDFs. That handoff, from document collection to structured underwriting input, is where most funders lose hours per deal.

How to Adjust Your Underwriting for the SBA Overflow

Flag the SBA Decline Context

The single most valuable piece of information you can capture from a displaced SBA borrower is why they were declined. Not every merchant will tell you directly, but the bank statements often reveal the answer. If you see consistent debt service payments to an SBA lender that suddenly stop, the merchant likely had a previous SBA relationship that deteriorated. If you see large, irregular deposits that do not match the business type, the SBA may have flagged potential revenue inflation.

Training your underwriting team, and your AI models, to look for these contextual signals is a meaningful upgrade to MCA underwriting best practices. It moves your process beyond simple cash flow math into genuine risk assessment.

Watch for Stacking Born From Desperation

Merchants who have been rejected by the SBA and are facing a cash crunch are prime candidates for stacking multiple MCA positions. They may accept offers from two or three funders simultaneously, each unaware of the others. The bank statements will show the evidence if you know where to look: multiple same-day ACH debits from different funding companies, declining average balances despite stable revenue, and deposit patterns that suddenly shift as the merchant juggles obligations.

Automated analysis catches these patterns faster than manual review. As we explored in our piece on preventing MCA stacking fraud with smarter bank verification, the key is systematic transaction categorization rather than spot-checking individual line items. When your system flags three concurrent ACH pulls from known MCA funders, you have an objective data point rather than a gut feeling.

Incorporate Seasonal Revenue Context

SBA underwriters often decline loans based on seasonal revenue volatility that falls outside their risk appetite. A landscaping company doing $120,000 per month in summer and $40,000 in winter may look risky to an SBA underwriter evaluating annual debt service coverage. For an MCA funder, that same seasonal pattern is perfectly manageable if the advance amount and remittance structure are sized correctly.

The critical underwriting question becomes: are you looking at four months of bank statements that capture the right season? If a displaced SBA borrower submits statements from their peak months, the revenue picture looks very different than if they submit from their trough. This is one reason collecting the last four months of statements, as Let's Submit's upload flow is designed to do, provides a more balanced view than the typical two-month request. The Small Business Administration itself has acknowledged that its program changes are concentrating approvals among larger borrowers, which means the seasonal small businesses flowing to MCA funders need underwriting frameworks that account for cyclicality rather than penalize it.

A Real-World Scenario: What the Overflow Looks Like in Practice

Consider a plumbing contractor doing $85,000 per month in revenue with six years in business. He applied for a $100,000 SBA 7(a) loan to purchase a new service vehicle and fund a marketing push. The SBA lender declined him because his existing equipment loan pushed his debt service coverage ratio below their threshold. His credit score is 680. His bank statements show steady deposits, two NSFs in the last 90 days, and an average daily balance of $14,000.

This merchant is a strong MCA candidate. The revenue supports an advance in the $60,000 to $80,000 range. The NSFs are manageable and likely tied to timing rather than chronic cash shortfalls. But here is the risk: this merchant is frustrated, potentially shopping three or four funders at once, and motivated to accept the first offer that comes through.

The funder who collects his documents fastest, verifies his bank statements accurately, and presents an offer within hours, not days, wins the deal. The funder who takes 48 hours to chase down bank statements by email loses to someone with a streamlined intake process. In 2026, that speed advantage is less about having more reps on the phone and more about having the right infrastructure: async document collection, AI-powered extraction, and clean handoff to underwriting.

This is the scenario playing out thousands of times per quarter as the SBA pipeline contracts. The merchants are real, the demand is real, and the competitive pressure is real. Your underwriting infrastructure either handles it or it does not.

Frequently Asked Questions

Why are SBA 7(a) loan counts declining?

SBA 7(a) loan counts fell over 27% in the most recent fiscal year, driven by tighter lending standards, policy changes that reduced the scope of eligible loans, and lender consolidation that favors larger ticket sizes. The average loan size rose 21%, indicating that smaller borrowers are being squeezed out of the program. Industry leaders like Lendesca CEO Chris Hurn have noted that these shifts are structural, not cyclical, meaning the decline is unlikely to reverse quickly.

How do displaced SBA borrowers affect MCA underwriting?

Displaced SBA borrowers bring different risk profiles than typical MCA applicants. They often have longer operating histories, more stable revenue, and expectations shaped by conventional lending. However, they may also carry existing debt obligations, seasonal volatility, or documentation issues that caused the SBA decline. MCA funders need to adjust their underwriting to account for this context rather than applying a one-size-fits-all approach.

What MCA underwriting best practices apply to SBA overflow merchants?

The most important practices include flagging the context of the SBA decline within bank statement analysis, watching for stacking behavior from merchants shopping multiple funders simultaneously, incorporating seasonal revenue patterns rather than penalizing cyclicality, and using automated bank statement analysis to maintain accuracy at higher volumes. Collecting at least four months of bank statements provides a more balanced revenue picture than shorter windows. Speed of document collection also matters, because displaced borrowers are often actively shopping competitors.

How does automated bank statement analysis help with higher MCA volume?

Automated bank statement analysis uses AI to categorize transactions, calculate average daily balances, flag NSFs and overdrafts, identify recurring debt obligations, and detect anomalies like potential stacking. This allows funders to process significantly more applications without adding headcount or sacrificing accuracy. For the SBA overflow specifically, automation is critical because displaced borrowers tend to submit more complex statements with higher transaction volumes that are time-consuming and error-prone to review manually.

Conclusion

The SBA 7(a) contraction is not a blip. A 27% drop in loan counts is a structural shift that is redirecting thousands of small businesses toward merchant cash advance funders every quarter. The funders who capture this demand profitably will be the ones who adapt their underwriting to the specific risk profile of displaced SBA borrowers, not the ones who simply process more applications through the same pipeline.

That adaptation starts with faster, more accurate document collection and bank statement verification. Let's Submit handles the intake bottleneck so your underwriting team can focus on credit decisions. Merchants upload bank statements, IDs, and signed applications from their phones. AI extraction structures the data before your team opens the file. Visit letssubmit.ca to see how async verification fits into your workflow and positions you to win the deals that the SBA is leaving behind.