How Square Loans' Sub-4% Loss Rates Expose the Bank Verification Software Gap for Funders

Square's Proprietary Data Moat and What It Means for Independent Funders

When Block CEO Jack Dorsey told shareholders in Q2 that "Square Loans are underwritten on data banks can't see and serve sellers banks won't," he wasn't just making a marketing claim. He was describing a structural advantage that every independent MCA funder should take seriously. Square Loans has posted loss rates below 4% through every cycle it has encountered, a track record that most alternative lenders would struggle to match. The reason is straightforward: Square sees every card swipe, every refund, every deposit pattern flowing through its own payment ecosystem. Independent funders relying on bank verification software for funders need to understand this gap, because closing it is now the central challenge of competitive MCA underwriting in 2026.

This article breaks down why Square's data advantage translates into lower losses, where that advantage leaves independent funders exposed, and how async bank verification technology, particularly AI-guided screen recording, gives funders without an embedded platform a credible path to better underwriting outcomes.

Why Embedded Lending Data Produces Lower Loss Rates

Real-Time Revenue Visibility vs. Static Snapshots

Square doesn't wait for a merchant to submit three months of bank statements. It already knows the merchant's daily sales volume, average transaction size, refund frequency, seasonal patterns, and customer concentration. That data is continuous, verified by the platform itself, and impossible for the merchant to manipulate. When Square decides to extend a loan or advance, it is effectively underwriting against a live feed of the merchant's economic activity.

Independent MCA funders, by contrast, typically start with a PDF bank statement, a signed application, and maybe a credit pull. Even when those documents are legitimate, they represent a static snapshot of a moment in time. A merchant whose revenue dropped 40% last week won't show that decline on a statement pulled two weeks ago. This lag is where losses concentrate.

Platform Control Over Repayment

Beyond underwriting, Square controls the repayment channel. It deducts a fixed percentage of each day's card sales automatically, adjusting the absolute dollar amount to match the merchant's actual revenue. If sales slow, collections slow proportionally, reducing the likelihood of default. Independent funders relying on fixed ACH debits face a fundamentally different risk profile. A bad month for the merchant doesn't automatically reduce the funder's collection attempt, it just increases the odds of an NSF return.

This combination of superior data and embedded repayment creates a compounding advantage. Square's sub-4% loss rates aren't the result of smarter people or better algorithms alone. They flow from a structural position that independent funders simply cannot replicate by hiring more underwriters or buying a better CRM.

How Bank Verification Software Closes the Data Gap

Moving Beyond Static Bank Statements

If independent funders can't match Square's embedded data access, the question becomes: what is the richest, most trustworthy signal available to them? The answer, increasingly, is verified screen recordings of live banking sessions. Unlike a PDF statement that can be edited in minutes with freely available tools, a screen recording of a merchant logging into their actual bank portal and scrolling through transactions in real time is extremely difficult to fabricate.

As we explored in our analysis of how AI detects fake banking sessions in screen recordings, AI vision models can now validate that a recorded session shows a genuine banking portal rather than a manipulated clone. The visual continuity of scrolling, the rendering behavior of real banking interfaces, and the consistency of transaction timestamps all provide signals that static documents cannot.

AI-Guided Verification in Practice

Exact Balance's approach to this problem is built around asynchronous, AI-guided screen recording. The workflow is simple but powerful. The funder creates a verification request specifying exactly what they need to see: account summaries, specific date ranges, transaction details, or outstanding obligations. The applicant receives a secure link, opens it in their browser, and records their live banking session with a floating AI coach that walks them through each required step.

No scheduling is involved. The applicant records at their convenience, and the underwriter reviews the recording whenever it fits their workflow. This async model is critical because it eliminates the time zone coordination, no-show calls, and back-and-forth rescheduling that slow traditional verification. While Square funds in hours using its embedded data, an independent funder using async verification can compress the gap from days to hours as well, without needing to own the merchant's payment stack.

What Screen Recordings Reveal That Statements Cannot

A verified screen recording captures several layers of information that a bank statement simply does not contain. First, it shows the merchant's actual banking environment: the institution, the interface, the account structure. Second, it reveals the full transaction feed in context, not just a summary but the granular detail that appears when a user clicks into individual transactions. Third, it captures behavioral signals. Does the merchant navigate their banking portal fluently, or do they hesitate and fumble as if encountering it for the first time?

These signals matter because sophisticated fraud schemes now target the exact verification steps that funders rely on. Scammers purchase aged business entities, fabricate bank statements, and present polished applications that pass standard document checks. A live banking session recording raises the bar significantly, requiring a fraudster to not only create fake documents but also build and operate a convincing fake banking portal in real time.

Real-World Implications for Independent MCA Funders

The practical challenge facing independent funders is not just fraud prevention. It is speed. Square's Q2 results show that embedded lenders are pulling further ahead on velocity. When a Square seller receives a loan offer inside their dashboard and can accept it with a single click, every hour an independent funder spends scheduling a verification call is competitive ground lost.

Consider a typical scenario. A restaurant owner in Toronto applies for a $75,000 advance through a broker. The broker submits the deal to three funders simultaneously. One funder requires a live verification call, which takes two days to schedule because the owner works lunch and dinner service. Another funder requests three months of PDF statements, which the owner downloads and emails the next morning. A third funder uses Exact Balance: the owner receives a secure link at 10 PM after closing, records a five-minute banking session on their phone, and the underwriter reviews it at 8 AM the next day.

The third funder gets a richer signal than the PDF-only funder and a faster turnaround than the live-call funder. That combination of speed and depth is what keeps independent funders competitive against platforms with embedded data advantages.

This competitive dynamic is playing out across the industry. Shopify Capital originated $1.4 billion in small business loans and MCAs in Q2 2026 alone, with its CFO noting that "Capital was a larger driver this quarter." Every dollar originated by an embedded platform is a dollar that an independent funder competed for and potentially lost. The funders who survive this consolidation will be those who find ways to verify merchant quality quickly and reliably without owning the merchant's entire commerce stack.

For Canadian funders specifically, the regulatory environment adds another dimension. As we discussed in our coverage of Shopify Capital's MCA-to-loan shift in Canada, the transition from advances to loans under amended regulations creates new documentation and compliance requirements. Async bank verification provides a timestamped, stored audit trail for every verification session, giving funders the compliance documentation they need without adding manual steps to the process.

Frequently Asked Questions

How do embedded lenders like Square achieve such low loss rates on business loans?

Embedded lenders achieve low loss rates primarily because they underwrite on proprietary transaction data generated within their own payment platforms. Square sees a seller's daily card volume, refund rates, seasonal trends, and customer patterns in real time. This continuous data stream eliminates the lag and manipulation risk inherent in static bank statements. Additionally, embedded lenders control the repayment channel by deducting a percentage of each day's sales automatically, which means repayment adjusts proportionally to the merchant's actual revenue and reduces default risk.

What is bank verification software for funders and how does it work?

Bank verification software for funders is technology that allows MCA lenders and alternative finance companies to verify a merchant's banking activity without relying solely on submitted documents. Exact Balance, for example, uses asynchronous screen recording: the applicant receives a secure link, records their live banking session in a browser, and the funder reviews the timestamped recording on demand. AI-guided coaching walks the applicant through required steps, and AI vision validates the authenticity of the banking portal shown in the recording. This approach provides richer verification data than static PDFs while eliminating scheduling delays.

Can independent MCA funders compete with Square and Shopify Capital on underwriting quality?

Independent funders cannot fully replicate the embedded data advantages of platforms like Square or Shopify Capital. However, they can close the gap significantly by using bank verification tools that capture live, verified banking data rather than relying on static statements. Screen recordings of real banking sessions provide visual proof of transaction authenticity that PDFs cannot match. Combined with async workflows that eliminate scheduling overhead, independent funders can approach the speed and confidence of embedded lenders without needing to own the merchant's payment infrastructure.

Why does asynchronous verification matter for MCA underwriting speed?

Asynchronous verification matters because the traditional alternative, live verification calls, introduces scheduling friction that directly slows funding decisions. Merchants operate during business hours, often in different time zones than the funder's underwriting team. Missed calls, rescheduled appointments, and no-shows can add days to a deal timeline. Async verification removes this bottleneck entirely. The applicant records their banking session whenever it suits them, and the underwriter reviews the recording whenever they are ready. This model keeps deals moving around the clock without requiring both parties to be available simultaneously.

Conclusion

Square's sub-4% loss rates are a benchmark, not a ceiling. They demonstrate what becomes possible when an underwriter has access to continuous, verified merchant data. Independent MCA funders will never own Square's payment ecosystem, but they don't have to accept a permanent data disadvantage either. Bank verification software that captures live, AI-validated screen recordings of real banking sessions gives funders a signal that is richer than static documents and harder for fraudsters to defeat.

The funders who thrive through this era of embedded lending consolidation will be those who verify faster, verify deeper, and maintain audit trails that satisfy both investors and regulators. Exact Balance was built for exactly this challenge. Visit exactbalance.ca to see how async bank verification fits into your underwriting workflow.