How SMB Lending Cooling Signals Reshape Bank Verification Software for Funders

SMB Lending Is Cooling. Here Is What That Means for MCA Funders.

During LendingTree's most recent earnings call, CEO Scott Peyree described a lending environment where fewer small merchants are actively looking for capital. "When the war started in March and gas prices went way up, that was a shock to the system," Peyree noted, adding that small business lending volumes have softened as uncertainty lingers. For MCA funders and ISOs who depend on a steady stream of applications, this shift demands attention. The question is no longer just "how fast can we fund?" but "how well do we verify every deal that lands on our desk?"

This is where bank verification software for funders becomes a strategic advantage rather than a back-office cost. When deal flow was abundant, a certain amount of underwriting sloppiness could be absorbed. A few bad deals in a portfolio of hundreds barely moved the needle. In a cooling market, the math changes. Each approval carries more weight. Each default stings harder. And each missed red flag in a bank statement represents capital that could have gone to a better merchant.

In this article, we break down why softening SMB demand forces MCA funders to rethink their verification workflows, what specific bank statement signals matter most during periods of economic stress, and how AI-powered extraction tools help funders maintain quality without sacrificing the speed that still wins deals.

Why Soft Markets Expose Weak Verification Processes

Fewer Applications, Higher Stakes Per Deal

The relationship between application volume and underwriting discipline is counterintuitive. Most funders assume that when pipelines are full, risk is highest because the pressure to approve quickly leads to shortcuts. That is partly true. But the opposite scenario is equally dangerous. When fewer merchants apply, sales teams face quota pressure that pushes borderline deals toward approval. Underwriters feel implicit pressure to "find a way" to make marginal applications work.

This is exactly the environment where fabricated or manipulated bank statements slip through. A merchant whose daily deposits have been declining for three months might still show a healthy average monthly balance if the underwriter only glances at summary figures. Without automated extraction that flags deposit trend lines, ending daily balances, and NSF patterns across all statement pages, these signals hide in plain sight. As we explored in our analysis of how AI fraud detection catches fabricated bank statements, the most effective manipulations target exactly the metrics that manual review tends to skip.

Economic Stress Changes Merchant Risk Profiles Overnight

Rising input costs, tariff uncertainty, and shifting consumer spending patterns do not affect all merchants equally. A restaurant that was a solid funding candidate six months ago may now show a very different cash flow picture. The challenge for funders is that traditional underwriting models often rely on trailing averages that smooth out recent deterioration.

In 2026, purpose-built bank verification tools need to surface not just what a merchant's cash flow looks like on average, but what it looks like right now. That means extracting and analyzing daily balance trajectories, comparing deposit frequency week over week, and flagging sudden changes in transaction patterns that suggest a business is under stress. This is precisely the kind of analysis that AI-powered document extraction handles efficiently. Rather than asking an underwriter to manually scan 90 days of statements looking for anomalies, machine learning models trained on MCA-specific data can flag the ten transactions or patterns that actually matter.

Broker Submission Quality Often Declines in Soft Markets

When merchants are harder to find, brokers cast wider nets. The quality of submissions tends to drop before the quantity does. ISOs may push applications with incomplete documentation, stale bank statements, or merchants that have already been declined elsewhere. For funders relying on manual intake processes, this creates a cascade of inefficiency: time spent chasing missing pages, re-requesting updated statements, and reviewing deals that should have been filtered out before reaching underwriting.

Asynchronous document collection solves the front end of this problem. Instead of accepting whatever a broker emails over and then spending hours sorting through attachments, platforms like Let's Submit give each applicant a secure upload link. The merchant submits their own documents directly. AI extraction pulls business information, financial data, and owner details automatically. By the time an underwriter sees the deal, it arrives structured, complete, and ready for analysis. This approach, which we detailed in our piece on building a scalable MCA application pipeline, becomes even more critical when every deal in the queue needs to count.

The AI Verification Signals That Matter Most in a Cooling Market

Not all bank statement data points carry equal weight during periods of economic softening. Funders who adjust their verification focus to match current conditions will outperform those running the same playbook they used during peak demand. Here are the specific signals that AI-powered bank verification should surface.

Deposit Velocity Trends Over 90 Days

A merchant's total deposits over three months can mask a sharp recent decline. If January showed $120,000 in deposits, February showed $95,000, and March showed $68,000, the quarterly total of $283,000 might still look acceptable in isolation. Automated extraction that charts deposit velocity week by week tells a much more urgent story. AI models can calculate the rate of decline and project forward, giving underwriters a concrete basis for adjusting offer terms or declining outright.

NSF and Overdraft Frequency Relative to Revenue

A single NSF event on a high-revenue account is noise. Five NSF events in a month on an account averaging $50,000 in deposits is a signal. The ratio matters more than the raw count, and calculating that ratio across dozens of statement pages is exactly the kind of tedious, error-prone work that automated analysis eliminates. Machine learning classifiers trained on MCA portfolios can benchmark a merchant's NSF-to-deposit ratio against historical default rates, producing a risk score that manual review simply cannot replicate at scale.

Ending Daily Balance Patterns

Merchants under financial stress often show a characteristic pattern: balances spike on deposit days and crater between them. This sawtooth pattern indicates that incoming revenue is immediately consumed by obligations, leaving no buffer. When AI extraction pulls daily ending balances and visualizes them, this pattern becomes immediately apparent. In a strong market, an underwriter might overlook it. In a cooling market, it is exactly the kind of signal that separates a profitable advance from a default.

Existing MCA Payment Detection

Stacking risk intensifies during downturns because merchants with existing advances who face declining revenue are more likely to seek additional funding to cover shortfalls. Detecting existing daily or weekly ACH debits that match MCA repayment patterns requires transaction-level analysis across every page of every statement. This is one of the highest-value applications of AI in bank verification: scanning thousands of transactions to identify patterns that suggest an applicant already has one or more active advances. We covered this challenge in depth in our article on preventing MCA stacking fraud with smarter bank verification.

Real-World Scenario: Underwriting the Same Merchant in Two Different Markets

Consider a hypothetical merchant, a mid-sized auto repair shop in the Northeast. Six months ago, this business showed $85,000 in average monthly deposits, two NSF events over 90 days, a stable ending daily balance averaging $8,500, and no detectable existing MCA payments. In a strong market with a full pipeline, this merchant would be a straightforward approval at favorable terms.

Now the same shop applies again. The latest statements show average monthly deposits of $71,000, declining each month. NSF events have doubled to four over 90 days. The average ending daily balance has dropped to $3,200. And there are now regular ACH debits of $480 per business day that look like MCA repayment.

An underwriter reviewing these statements manually might catch the deposit decline if they are thorough. They might notice the NSFs. But would they catch the subtle ACH pattern on page seven of a 15-page PDF? Would they calculate the deposit velocity trend? Would they compare the ending balance trajectory against the shop's historical baseline?

With AI-powered bank verification, all of these signals surface automatically at the point of document ingestion. The underwriter receives a structured summary that highlights each concern, quantifies the change from prior periods, and flags the suspected stacking. Instead of spending 30 minutes on manual review and still missing half the picture, the decision takes five minutes and covers everything. According to data from the Federal Reserve's Small Business Credit Survey, small business financing gaps often widen during periods of economic uncertainty, which means the merchants most likely to apply during soft markets are also the ones most likely to present complex risk profiles.

This scenario illustrates why bank verification software for funders is not just a technology upgrade. It is a risk management imperative, especially when the margin for error narrows. LendingTree's latest report on softening SMB demand confirms that the merchants still seeking capital in this environment are a different population than those who applied during boom periods. Treating them identically is a recipe for portfolio losses.

Frequently Asked Questions

How does a cooling lending market affect MCA underwriting?

A cooling market reduces the number of applications in the pipeline, which increases the financial impact of each individual funding decision. Funders face more pressure to approve marginal deals to maintain volume, while the merchants who do apply are more likely to be under financial stress. This combination makes thorough bank verification more important, not less, because the cost of a default is proportionally higher when overall deal flow is down.

What bank statement signals indicate a merchant is under financial distress?

The most reliable distress signals include declining deposit velocity over 30, 60, and 90 day windows; increasing NSF or overdraft frequency relative to total deposits; shrinking ending daily balances that show a sawtooth pattern of spikes and drops; and the appearance of regular ACH debits that match MCA repayment schedules. AI-powered bank statement analysis can surface all of these signals automatically, while manual review frequently misses two or more of them.

Can bank verification software detect MCA stacking?

Yes. Advanced bank verification platforms use transaction-level analysis to identify recurring ACH debits that match common MCA repayment patterns, such as fixed daily or weekly withdrawals. Machine learning models can distinguish these from regular business expenses like rent or payroll by analyzing amount consistency, timing, and counterparty data. Detecting stacking is one of the highest-value capabilities of automated bank statement analysis, particularly during economic downturns when stacking risk increases.

How does async document collection help during low-volume periods?

Asynchronous document collection, where applicants upload documents through a secure link on their own time, reduces drop-off rates during intake. When fewer merchants are applying, losing even one qualified applicant to a clunky submission process is costly. Async collection also ensures documents arrive complete and in the right format, eliminating the back-and-forth that delays underwriting. Let's Submit's applicant upload portal handles this seamlessly, keeping deals moving even when overall market activity slows.

Conclusion

Softening SMB lending demand is not a reason to relax. It is a reason to sharpen every part of the verification and underwriting process. When deal flow contracts, the quality of each funding decision matters exponentially more. Bank verification software for funders delivers the precision that manual processes cannot, surfacing declining deposit trends, stacking indicators, and distress signals before capital goes out the door.

Let's Submit helps MCA funders maintain rigorous verification standards without slowing down the deals that deserve to close. From async document collection to AI-powered data extraction, the platform is built for exactly the kind of market we are in now: one where getting it right matters more than getting it fast. Visit letssubmit.ca to see how async verification fits into your workflow.