How 76% of Small Businesses Bypassing Banks Reshapes Bank Verification Software for Funders

Small Businesses Are Skipping Banks, and MCA Funders Are Absorbing the Complexity

A striking data point from OnDeck's latest small business survey, published through PR Newswire, reveals that 93% of small businesses expect growth in 2026, while 76% now bypass banks entirely when seeking funding. That second number is the one that should keep MCA underwriters up at night. Because every business owner who walks past their local bank branch and applies for a merchant cash advance instead brings a financial profile that traditional bank verification software for funders was never designed to handle.

These applicants do not have clean, single-institution deposit histories. Their revenue arrives through payment processors, marketplace payouts, invoicing platforms, and sometimes cash deposits scattered across multiple accounts. For funders still relying on manual bank statement review or phone-based verification calls, this shift creates a bottleneck that slows deal flow and increases the risk of misjudging an applicant's true cash position.

This article breaks down what the bank-bypass trend means for MCA underwriting workflows, why traditional verification methods fall short with this new borrower profile, and how AI-powered bank statement analysis closes the gap.

Why the New Borrower Profile Breaks Traditional Verification

Fragmented Cash Flows Across Multiple Channels

When a small business owner relies on a single bank for all operations, their statements tell a relatively clean story. Deposits line up with revenue. Debits reflect predictable expenses. An underwriter can scan three months of statements and quickly assess average daily balance, monthly revenue, and expense ratios.

The 76% who bypass banks do not look like this. A restaurant owner might receive revenue through Square deposits, DoorDash payouts, and direct cash deposits, each hitting different accounts on different schedules. An e-commerce seller could have Shopify payouts arriving weekly, Amazon settlements biweekly, and supplier payments pulling from a separate checking account. These fragmented cash flows create bank statements that look chaotic to a human reviewer but contain perfectly healthy business fundamentals once the data is properly categorized.

The challenge for bank verification software is parsing these diverse transaction types accurately. Generic OCR tools that extract text from PDFs miss the context. They cannot distinguish between a Stripe payout and a personal transfer, or recognize that a $4,200 deposit labeled "SHOPIFY PAY" represents recurring e-commerce revenue rather than a one-time event. Purpose-built AI models trained on MCA-specific document patterns handle this far more reliably, as we explored in our analysis of how purpose-built AI models outperform general LLMs in MCA document verification.

Non-Standard Account Structures

Applicants who bypass banks often maintain accounts at neobanks, credit unions, or fintech platforms that produce statements in non-standard formats. A statement from Mercury or Relay looks nothing like one from Chase or Bank of America. Column layouts differ. Transaction descriptions follow different conventions. Date formats vary. Some platforms do not even produce traditional monthly statements, offering instead CSV exports or dashboard screenshots.

Manual underwriting teams spend enormous time simply figuring out the format before they can begin analyzing the data. This is where automated bank statement analysis delivers its clearest advantage. AI extraction engines can be trained to recognize hundreds of statement formats, mapping each one to a standardized data model that underwriters can review consistently regardless of the source institution.

Multiple Revenue Streams Require Holistic Analysis

A single-bank borrower might show one clear revenue line. A bank-bypass borrower might show five. The underwriting question shifts from "what is this business's monthly revenue" to "what is the aggregate cash flow across all sources, and how stable is each one." This requires not just extracting numbers but categorizing and correlating them across documents.

Consider a merchant who submits three months of statements from two different accounts plus a payment processor summary. The underwriter needs to reconcile deposits in the bank statements against the processor payouts, identify any timing gaps, and determine whether the stated revenue aligns with what the accounts actually show. Doing this manually for every application is the reason deals die in the pipeline. Doing it with AI-powered extraction and categorization is the reason some funders close in hours while others take days.

How AI-Powered Verification Handles the Complexity at Scale

Automated Transaction Categorization

Modern AI extraction goes beyond reading numbers off a page. Machine learning models classify each transaction into categories: revenue deposits, loan payments, NSF fees, transfers between accounts, tax payments, and operating expenses. This categorization happens automatically during the extraction step, so by the time an underwriter sees the data, it is already organized into the metrics that matter for MCA decisioning.

For the bank-bypass borrower with fragmented deposits, this means the system can aggregate revenue across multiple sources, flag negative-day balances, and calculate true average daily balances without requiring the underwriter to manually cross-reference documents. The time savings compound with volume. A funder processing 50 applications per day cannot afford 30 minutes of manual reconciliation on each one.

Async Document Collection Reduces Friction

The bank-bypass trend also changes how applicants submit documents. Business owners who manage their finances through multiple platforms often do not have a single PDF they can send. They need to download statements from each source, sometimes export data in unfamiliar formats, and figure out which documents the funder actually needs.

This is exactly where async bank verification for MCA transforms the experience. Instead of requiring applicants to email documents or fax them in a specific order, platforms like Let's Submit provide a single secure upload link. The applicant uploads whatever documents they have, whenever they have them. AI extraction processes each document as it arrives, and the funder's dashboard updates in real time. No phone calls. No chasing missing pages. No deals stalling because the applicant could not figure out how to download a statement from their neobank.

The async model also handles the common scenario where an applicant has documents ready from one account but needs a day to pull statements from another. Traditional synchronous workflows force the funder to wait until everything arrives before beginning review. Async processing lets the team start extracting and analyzing data from the first document immediately.

Fraud Detection Adapts to Non-Traditional Profiles

Fragmented financial profiles create a double-edged risk. On one side, legitimate businesses look more complex than they used to, and rigid verification rules may reject good applicants. On the other side, fraudsters exploit the complexity by submitting fabricated statements that mimic the messy patterns of real multi-source businesses.

AI fraud detection models address both problems. They learn the statistical signatures of genuine multi-platform revenue patterns and flag documents that deviate in subtle ways: font inconsistencies within a PDF, running balances that do not reconcile, transaction timestamps that fall outside normal processing windows, or deposit amounts that cluster too perfectly. These signals are invisible to human reviewers scanning at speed but detectable by models trained on thousands of real and fabricated statements.

We covered the broader fraud detection landscape in detail in our piece on how AI fraud detection catches fabricated bank statements in business lending. The bank-bypass trend makes those capabilities even more critical, because the baseline complexity of legitimate applications has risen, narrowing the gap between what looks normal and what looks suspicious.

What This Means for MCA Funders Competing on Speed

Enova's record $1.7 billion in Q1 2026 business loan originations illustrates where the market is heading. High-volume funders are winning because they process applications faster without sacrificing underwriting quality. When 76% of your applicant pool has a more complex financial profile than they did three years ago, the only way to maintain speed is through automation.

Funders who still rely on manual intake workflows face a compounding disadvantage. Each application takes longer to process. Underwriters spend more time on data entry and less on actual credit analysis. Deals that sit in review for 48 hours get funded by a competitor in 12. The applicant does not care that their multi-source bank statements were harder to parse. They care about who puts money in their account first.

This competitive pressure is why bank verification software for funders has moved from a back-office efficiency tool to a front-line competitive weapon. The funder with the fastest, most accurate extraction pipeline wins the deal. Period.

Let's Submit was built for exactly this scenario. A single upload link collects all documents asynchronously. AI extraction parses bank statements, applications, and ID documents regardless of format or source institution. The underwriting team reviews clean, categorized data on a real-time dashboard. The entire flow, from applicant submission to underwriter review, can happen without a single phone call or email thread. For funders processing the new generation of bank-bypass borrowers, that workflow difference is the difference between funded deals and lost ones.

Frequently Asked Questions

Why are small businesses bypassing banks for funding?

Small businesses increasingly skip banks because traditional lending processes are slow, documentation requirements are heavy, and approval rates for smaller loan amounts remain low. Alternative funders, including MCA providers, offer faster decisions and fewer paperwork hurdles. The OnDeck 2026 survey found that 76% of small businesses now seek capital outside the banking system, driven by speed and accessibility.

How does the bank-bypass trend affect MCA bank verification?

When applicants do not rely on a single traditional bank, their financial data is spread across multiple accounts, payment processors, and neobank platforms. This creates bank statements with irregular deposit patterns, non-standard formats, and multiple revenue streams that are harder to parse manually. MCA funders need bank verification software that can handle diverse document formats, categorize transactions automatically, and reconcile data across sources.

Can AI-powered extraction handle statements from neobanks and fintech platforms?

Yes. AI extraction engines trained on diverse financial document formats can process statements from neobanks like Mercury, Relay, and Novo alongside traditional bank statements. The key is using models specifically trained on the range of formats MCA funders encounter, rather than generic OCR tools designed for standardized documents. Let's Submit's AI extraction handles hundreds of statement formats and normalizes the data into a consistent structure for underwriter review.

What is async bank verification and why does it matter for MCA lenders?

Async bank verification allows applicants to upload documents on their own time through a secure link, rather than submitting everything at once during a live session or phone call. Documents are processed by AI as they arrive, and the funder's team can begin reviewing extracted data before the full package is complete. This approach reduces friction for applicants with complex, multi-source financial profiles and eliminates the back-and-forth that stalls traditional intake workflows. We covered the broader case for this approach in our analysis of how speed to lead depends on bank verification software for funders.

Conclusion

The era of the single-bank small business borrower is ending. With three out of four applicants now arriving from outside the traditional banking system, MCA funders face a choice: adapt their verification infrastructure or lose deals to competitors who already have. AI-powered bank statement analysis, async document collection, and intelligent transaction categorization are no longer optional upgrades. They are the foundation of a competitive underwriting operation.

Let's Submit gives MCA funders the tools to handle this new reality. One secure link for applicants. AI extraction that works across any document format. A real-time dashboard that turns complex, multi-source financial data into clear underwriting insights. Visit letssubmit.ca to see how async bank verification fits into your workflow and start processing the next generation of MCA applications faster.