How LendingTree's MCA Growth Signal Changes Bank Verification Strategy for Funders

LendingTree Says the MCA Market Is Growing. Are You Ready for the Volume?

"The merchant cash advance market is a strong market that is growing," said LendingTree CFO Jason Bengel during the company's Q4 2026 earnings call. That single sentence should be a wake-up call for every MCA funder who still processes bank statements by hand. When a publicly traded referral platform with millions of monthly visitors signals that it is doubling down on small business financing, the downstream effect is clear: more applications are coming. The question is whether your bank verification software for funders can handle it.

For the past several years, MCA funders have competed primarily on speed. Whoever approves and funds fastest wins the deal. But speed without infrastructure is chaos. Applications pile up in inboxes, bank statements get misrouted, and underwriters spend hours manually keying in deposit totals instead of actually assessing risk. LendingTree's growth signal makes this problem urgent, not theoretical. In this article, we break down what the referral-driven volume surge means for your verification workflow, where manual processes will crack under pressure, and how to build a bank verification stack that scales with demand.

Why Referral-Driven Volume Breaks Manual Bank Verification

Referral Platforms Are Changing How Deals Arrive

LendingTree is not the only platform leaning into MCA. The broader trend across financial referral marketplaces is a shift toward small business products. CEO Scott Peyree noted during the same earnings call that LLM-driven referrals are producing "high-intent consumers" who convert at higher rates than traditional search traffic. NerdWallet's CEO echoed the same finding. This means funders connected to these platforms will see not just more applications, but applications from borrowers who are further along in their decision-making process. They expect fast answers.

The problem is that most MCA funders still receive documents through a patchwork of email attachments, broker portals, and even fax. When a referral platform sends a surge of qualified applicants, the intake bottleneck becomes the rate-limiting step. Underwriters cannot review what they cannot find, and they cannot find what is buried in a cluttered inbox. As we explored in our analysis of why MCA lenders lose deals to slow application intake, every hour of delay increases the probability that the applicant funds with a competitor.

Manual Bank Statement Review Does Not Scale Linearly

Consider a typical underwriter's bank verification workflow. They receive three months of bank statements as PDFs. They open each one, scan for average daily balances, look for NSF fees, check for evidence of existing MCA positions, and manually record figures in a spreadsheet or CRM. A single application might take 30 to 45 minutes of focused review. At 10 applications per day, that is a full workday consumed by data entry before any actual credit analysis begins.

Now imagine LendingTree's referral pipeline sends you 30 applications in a day instead of 10. Manual verification does not scale linearly because fatigue introduces errors. An underwriter who has been staring at bank statements for six hours will miss an NSF pattern on page 14 of a 20-page PDF. They will transpose a deposit figure. They will overlook a daily balance dip that signals cash flow distress. The cost is not just slower processing; it is worse decision-making.

Fraud Risk Rises in Lockstep With Volume

Higher volume from referral channels also introduces new fraud vectors. When applications arrive pre-qualified from a third party, there is an implicit trust bias. Underwriters may scrutinize these submissions less carefully because a reputable platform referred them. This is exactly the dynamic that fraudsters exploit. Altered bank statements, synthetic identity documents, and MCA stacking schemes are harder to detect when your team is rushing through a backlog.

The Consumer Financial Protection Bureau has noted that small business lending fraud complaints have been rising, particularly in segments where automated verification has not kept pace with application volume. Funders who rely on eyeball verification are playing a losing game as deal flow accelerates.

Building a Bank Verification Stack That Scales With MCA Growth

Asynchronous Document Collection Eliminates the Intake Bottleneck

The first choke point in any verification workflow is getting the documents in the door. Chasing applicants for missing bank statements through email threads and phone calls is the single biggest source of deal friction. An asynchronous approach flips this dynamic. Instead of your team collecting documents, the applicant uploads them at their convenience through a secure portal link.

Let's Submit was built around this exact principle. You send one link. The applicant uploads their bank statements, voided checks, business applications, and ID documents. Everything arrives in a structured dashboard, tagged to the right application, with no manual sorting required. For funders seeing a surge from referral platforms like LendingTree, this means the intake process scales without adding headcount. Whether you receive 10 or 100 applications in a day, the collection workflow remains the same.

AI-Powered Extraction Replaces Manual Data Entry

Once documents are collected, the next bottleneck is extracting usable data. This is where AI-powered document extraction earns its keep. Modern OCR combined with machine learning models can parse bank statement PDFs and pull structured data: opening balances, closing balances, total deposits, total withdrawals, individual transaction details, and account holder information. The accuracy of these systems in 2026 has improved dramatically, particularly for the standardized formats that major banks use.

Let's Submit's AI extraction layer processes uploaded documents automatically, pulling business information, financial figures, and owner details into a reviewable format. The critical distinction here is that AI does the data entry while humans do the judgment. Your underwriter is not typing numbers into a spreadsheet. They are reviewing pre-populated fields, flagging anomalies, and making credit decisions. This division of labor is what allows a team of three to process the volume that previously required eight.

We have covered the broader impact of this approach in our deep dive on how to reduce manual data entry in MCA lending, and the principle holds even more firmly as referral-driven volume grows.

The Human Review Layer Catches What AI Misses

No responsible funder should rely on AI extraction alone. Bank statement fraud has become more sophisticated, with altered PDFs that pass basic OCR checks but contain manipulated figures. The correct architecture is AI extraction for speed paired with human review for accuracy. Your underwriter should see the extracted data alongside the source document, able to click into any field and verify it against the original PDF.

This hybrid model is faster than fully manual review by an order of magnitude, yet it retains the judgment that pure automation cannot provide. An experienced underwriter can spot a font inconsistency, an unusual transaction pattern, or a balance that does not reconcile, things that even advanced AI models sometimes miss. The goal is not to replace human expertise. It is to free that expertise from the drudgery of data entry so it can focus on pattern recognition and risk assessment.

What This Looks Like in Practice: A Referral Surge Scenario

Imagine you are a mid-size MCA funder. You have been averaging 15 applications per day, and your team of four underwriters handles the flow comfortably with manual processes. Then you sign a referral agreement with a major marketplace. Within two weeks, your daily application count jumps to 40.

Without automation, you have three options: hire more underwriters (expensive and slow), work overtime (unsustainable), or let applications queue up (deals lost). None of these are viable long-term strategies. With a platform like Let's Submit in place, the scenario plays out differently. Applicants upload documents through their unique links. AI extracts the key financial data. Your four underwriters now spend their time reviewing pre-structured files instead of manually processing raw PDFs. Throughput triples without any new hires.

The competitive advantage compounds over time. Faster verification means faster funding offers. Faster offers mean higher close rates. Higher close rates mean better unit economics, which justify more marketing spend, which brings more applications. This flywheel only works if the verification layer can keep pace. That is why LendingTree's growth confirmation matters: it signals that the application volume flywheel is accelerating across the industry, and funders without scalable verification will be left behind.

QuickBooks Capital, which originated $1.3 billion in business loans last quarter alone, has already demonstrated that platforms with embedded data access and automated underwriting can operate at massive scale. Independent MCA funders do not have the luxury of a captive data ecosystem, but they can replicate the efficiency by pairing smart document collection with AI extraction tools purpose-built for lending workflows.

Frequently Asked Questions

What is bank verification software for funders?

Bank verification software for funders is a category of tools that automate the collection, parsing, and analysis of bank statements submitted during the loan or MCA application process. Instead of manually reviewing PDF bank statements, funders use software that extracts key financial data like balances, deposits, withdrawals, and transaction patterns. Platforms like Let's Submit combine asynchronous document collection with AI-powered extraction so that underwriters receive structured, reviewable data rather than raw files.

How does referral volume from platforms like LendingTree affect MCA underwriting?

Referral platforms generate bursts of high-intent applications that arrive faster than organic deal flow. This puts pressure on every step of the underwriting process, especially bank statement review, which is traditionally the most time-consuming manual task. Funders without automated verification tools face a choice between slower turnaround times, which costs deals, or rushed reviews, which increases default and fraud risk. Automated bank verification allows funders to absorb volume spikes without sacrificing quality.

Can AI detect fraud in bank statements submitted for MCA applications?

AI can flag many common indicators of bank statement fraud, including inconsistent fonts, irregular spacing, transaction patterns that do not match stated revenue, and metadata anomalies in PDF files. However, sophisticated fraud still requires human review. The most effective approach combines AI extraction and anomaly detection with an experienced underwriter who reviews flagged items against the original source documents. This hybrid model catches more fraud than either approach alone.

How long does automated bank verification take compared to manual review?

Manual bank statement review typically takes 30 to 45 minutes per application for a three-month statement set. Automated extraction with AI can reduce the data entry portion to under five minutes, with the underwriter spending another 10 to 15 minutes on review and validation. For a funder processing 30 or more applications per day, this difference translates to saving dozens of labor hours each week, enabling the same team to handle significantly higher volume.

Conclusion

LendingTree's public confirmation that the MCA market is growing is not just a headline. It is a signal that application volume across the industry is heading up, driven by referral platforms, LLM-powered search, and increased small business demand. Funders who are still manually processing bank statements will hit a wall. Those who invest in scalable bank verification software will capture the opportunity.

Let's Submit gives MCA funders the infrastructure to handle this growth: one link for document collection, AI-powered data extraction, and a clean dashboard for human review. No more chasing applicants for missing pages. No more deals dying in email threads. Visit letssubmit.ca to see how async bank verification fits into your workflow and start processing applications at the speed your business demands.