AI Payments + Embedded Financing: The Growth Flywheel

A 27 percentage point growth delta is hard to ignore.

That’s what a two-year randomized trial found when comparing similar businesses on the same payments platform—those that accepted embedded financing versus those that didn’t. The businesses that took financing grew their on-platform revenue 27 percentage points faster on average, and the top 10% saw an average growth improvement of 211 percentage points.

This matters for anyone building or running payments, platforms, or fintech infrastructure—especially right now. December is when CFOs look at 2025 performance, teams finalize 2026 budgets, and operators ask the same question: Where’s the next step-change in growth going to come from? The answer often isn’t “more ad spend.” It’s fixing the infrastructure that decides who gets approved, how fast money moves, and how safely revenue can scale.

This post is part of our AI in Payments & Fintech Infrastructure series, so I’m going to take a stance: embedded lending works best when it’s glued to AI-powered payments infrastructure—risk, routing, fraud, and cash flow data. Payments tell the truth about a business in near real time. That truth is exactly what financing systems need.

What the 27-point growth result actually proves

The important detail isn’t just the number—it’s the method. A randomized controlled trial is designed to isolate cause and effect.

Correlation is cheap. Causation is rare.

Most lending “success stories” suffer from a simple problem: strong businesses are more likely to qualify for financing and more likely to grow anyway. If you only compare borrowers to non-borrowers, you risk measuring selection bias, not impact.

A randomized design avoids that trap by comparing businesses with similar credit, revenue, and longevity profiles where access or exposure to financing is controlled. In practical terms, it’s one of the cleanest ways to answer: Did the financing itself change outcomes?

The 27 percentage point average boost (in a 2023–2025 study window) also matters because it lands in a more “normal” macro environment than the 2020–2021 period, when ecommerce spikes and pandemic distortions made almost every growth chart weird.

Why the effect is smaller than 2020–2021—and why that’s good news

Earlier results in a 2020–2021 window showed a much larger effect (over 100 percentage points). A healthy interpretation is:

• 2020–2021 was an outlier period with unusual demand swings.
• 2023–2025 results are a sturdier baseline because growth happened under tighter credit conditions and more constrained consumer demand.

If you’re building fintech infrastructure, this is the takeaway: embedded finance can produce measurable lift even when the macro environment isn’t doing you favors.

The real mechanism: payments data turns lending into infrastructure

Embedded lending isn’t magic. It’s a data and distribution advantage.

Traditional small business lending typically relies on:

• Slow document collection (bank statements, tax returns, forms)
• Backward-looking underwriting
• Credit scores that underserve thin-file businesses
• Manual review queues

Payments-native financing flips that.

Payments are a live underwriting feed

When you run payments for a business, you can observe:

• Sales velocity (day-to-day changes, not quarterly summaries)
• Seasonality patterns (holiday peaks, slow months)
• Refund and dispute rates
• Customer concentration risk
• Cross-border demand and currency mix

This is where AI in payments infrastructure shows up in a practical way:

• Fraud detection reduces loss rates and stabilizes revenue signals.
• Smart authorization and routing improve acceptance, lifting top-line revenue and reducing false declines.
• Risk models can update as the business changes, not once a year.

Here’s the connective tissue: better payments performance produces better underwriting signals, which can produce faster and fairer access to capital, which can fund growth initiatives that increase payments volume. That’s the growth flywheel.

Speed is part of the product

A striking operational point: embedded financing can deliver funding in 1–2 days on average, compared to 14–40 days at many traditional banks.

Speed isn’t a convenience feature; it changes what businesses can do.

• A retailer can buy inventory before a seasonal surge.
• A SaaS company can hire a key engineer before churn rises.
• A field service business can purchase equipment before schedules back up.

If capital arrives after the moment passes, it’s not growth capital—it’s paperwork.

Who gets the biggest lift from financing (and why)

Averages hide the real story. The results vary widely, and that variation is the playbook.

Small businesses see outsized growth because they’re the most constrained

Among the smallest businesses (processing roughly $3,000 to $76,000 annually on-platform), financing drove a 33–43 percentage point average growth boost compared to peers.

That makes sense. When you’re small, you don’t just lack money—you lack slack. A single constraint (inventory, a part-time hire, a marketing test budget) can cap growth.

One detail operators should notice: even businesses with low or unavailable credit scores still showed an 11–18 percentage point average boost after accepting offers.

That’s a strong signal that payments-based underwriting can widen access without waiting for traditional credit files to catch up.

The “use of funds” is the hidden underwriting feature

Survey follow-ups found something operators often miss: the growth outcome correlated heavily with what businesses planned to do with the money.

Among businesses with strong credit, those using funds for growth-oriented projects (new products, expansion, scaling operations) saw 70–95 percentage point boosts on average.

My opinion: this is where the next generation of AI lending will focus—not just predicting default risk, but predicting ROI pathways.

A practical lens:

• Defensive financing: smoothing cash flow gaps, covering payroll, paying suppliers late.
• Offensive financing: inventory expansion, international servers, marketing experiments, hiring.

Both can be rational. But if your goal is revenue acceleration, you should build product flows that nudge borrowers toward measurable growth deployments, then help them track results.

What platforms and marketplaces should copy (without copying blindly)

If you run a platform—marketplace, vertical SaaS, creator platform, B2B ordering network—you’re sitting on a financing opportunity. But you shouldn’t ship “loans” as a feature. You should ship a capital layer inside your operating system.

Start with three infrastructure questions

1. Do you have stable revenue signals? If your platform’s payments data is fragmented, late, or off-platform, underwriting will be noisy.

2. Can you manage risk without punishing good users? AI-driven fraud prevention and dispute controls matter because they protect both the lender and the honest merchant.

3. Can you automate the boring parts? The value of embedded finance collapses if approvals take weeks. Automation needs to cover decisioning, KYC/KYB, offer presentation, and repayment.

Embed financing where the business already makes decisions

The best placement for financing offers is not “a loans tab.” It’s inside moments like:

• Inventory reorder screens
• Ad spend and campaign tools
• Hiring and scheduling modules
• Subscription upgrades
• International expansion settings

Why? Because the business owner can connect the funding to a concrete plan in the same workflow. That increases responsible uptake and improves outcomes.

Use AI to create “guardrails,” not just approvals

For lead-gen audiences building fintech infrastructure, here’s what I’ve found works: treat AI as a risk and performance co-pilot.

Examples of guardrails that reduce regret and increase ROI:

• Cash flow forecasting that shows repayment impact under conservative revenue assumptions
• Offer sizing based on seasonality (smaller offers heading into slow periods)
• Anomaly detection that flags unusual refund spikes before they become underwriting events
• Fraud scoring that informs both payment acceptance and financing risk

In other words: don’t build embedded lending as a separate product. Build it as a set of infrastructure services that share signals with your payments stack.

People also ask: common questions about embedded financing + AI payments

Is faster growth “guaranteed” if a business takes financing?

No. The data shows an average lift and large variation. Growth depends on whether capital goes into initiatives that create revenue and whether the underlying payments operation can scale without fraud, chargebacks, or acceptance issues.

Why would AI in payments affect access to financing?

Because underwriting quality depends on signal quality. AI-driven fraud prevention, smart routing, and dispute reduction make revenue signals more reliable—so lenders can make faster decisions with fewer false negatives.

What should operators measure after rolling out embedded lending?

Track the full loop:

• Offer exposure → acceptance rate
• Time to funding
• Incremental revenue growth vs matched cohort
• Repayment performance
• Fraud/chargeback deltas
• Merchant retention (financing can increase stickiness when done responsibly)

If you only track repayment, you’re missing the point. The product promise is growth.

Where this goes next: financing becomes a native layer of the payments stack

The World Bank has estimated a $5.7 trillion funding gap for small businesses in developing economies. Closing even a slice of that gap requires distribution (reaching SMBs where they already work) and underwriting (making decisions with limited traditional credit history). Payments platforms can do both.

The bigger shift, though, is architectural: payments, risk, and financing are converging into one infrastructure layer, increasingly mediated by AI. When transaction routing, fraud controls, and credit decisioning share signals, businesses get three compounding advantages: higher acceptance, lower loss, and faster access to growth capital.

If you’re evaluating embedded lending—either as a business owner deciding whether to accept an offer, or as a platform leader deciding whether to launch a program—don’t start with “Do we want to be in lending?” Start with this:

Do we want our best customers to wait 14–40 days for capital while their opportunity window closes?

That question tends to make the roadmap pretty clear.