Payments as Your Data Advantage

Every transaction is a signal. How transaction data becomes the foundation for AI-driven insights, the underwriting moat for embedded finance, and the asset that turns a vertical SaaS company into a fintech.

21 min read

The data you’re already collecting

If your embedded payments program is operating at the median attach rate on a base of 1,000+ merchants, you are generating a payment data asset that most financial institutions — banks, lenders, insurers — would pay significant sums to access. The difference is that you have it natively, in real time, and in the context of your merchants’ actual business workflows.

The data taxonomy breaks into four layers, each with distinct analytical applications.

Transaction-level data

• Amount, currency, and payment method (card, ACH, wallet).

• Timestamp (date, time, day of week, seasonality).

• Merchant ID and location (for multi-location businesses).

• Card type (credit vs. debit; consumer vs. commercial).

• Authorization response codes (approved, declined, referral, velocity flag).

• Settlement timing and funding speed.

• Interchange category and rate.

Customer-level data

• Purchase frequency and recency (RFM signals).

• Average transaction value and lifetime spend.

• Payment method preferences and changes over time.

• Payment failure rates (potential churn signal).

• Card-on-file storage and update patterns.

Business-level data

• Revenue trends (weekly, monthly, annual run rate).

• Seasonal patterns (peaks, troughs, growth trajectory).

• Cash flow cycles (time from service delivery to payment collection).

• Growth trajectory (volume velocity, new customer acquisition rate).

• Payment method mix and instrument preferences.

Risk-level data

• Chargeback patterns (frequency, categories, dispute resolution rates).

• Fraud signals (authorization decline rates, velocity anomalies, BIN-level patterns).

• Decline rates by card type and payment method.

• Refund rates and reversal patterns.

This data inventory — all of it generated as a byproduct of running an embedded payments program — is the raw material for a suite of AI-driven applications that create value for your platform, your merchants, and ultimately your merchants’ end customers.

How AI transforms payment data into insights

The six applications below represent the current state of AI-powered payment intelligence across leading embedded finance platforms. Each builds on the data taxonomy above and generates measurable business value.

1. Customer intelligence

Machine learning models trained on purchase frequency, average spend, payment method changes, and payment failure rates can identify at-risk customers — both end consumers for merchants and merchants themselves for the platform — with significantly greater precision than survey-based or support-ticket-based approaches.

Churn prediction models using payment data typically demonstrate 20–30% improvement in predictive accuracy over behavioral-only models (logins, feature usage). The reason: payment behavior is harder to mask than product usage. A contractor who stops processing payments through your platform is already partially gone — even if they are still logging in. Catching that signal early enables targeted retention intervention.

2. Fraud detection

Real-time transaction risk scoring is one of the most mature AI applications in payments. Stripe Radar, which processes hundreds of billions of dollars annually across millions of businesses, uses machine learning trained on network-wide transaction data to assign risk scores to each payment within milliseconds.

For vertical SaaS platforms, the advantage is vertical specificity. A model trained on restaurant payment patterns knows that a $1,200 transaction at 2:00 a.m. is anomalous in ways that a general-purpose fraud model would not flag. Your platform’s training data is more concentrated in your vertical’s behavioral norms — which means more accurate risk scoring with fewer false positives. The Worldpay/Payrix Vertical SaaS Benchmarking Study confirms that vertical SaaS platforms achieve a 0.04% all-client chargeback rate, well below general industry benchmarks, partly because of this vertical specificity.

3. Cash flow prediction

One of the highest-value applications for SMB merchants is revenue forecasting. Most small businesses lack the financial infrastructure to produce accurate cash flow projections. Your platform, with 12–24 months of transaction history, can generate these projections automatically.

A field services platform with HVAC contractor data knows when seasonal installation peaks drive 40% of annual revenue. A restaurant platform knows when Tuesday lunch is consistently slow and Friday dinner drives disproportionate weekly revenue. These predictions — delivered as a merchant-facing dashboard — create a financial intelligence product that no bank can replicate from statement data alone.

4. Credit underwriting

This is where payment data creates the most direct competitive moat against traditional financial institutions. FICO scores are backward-looking, infrequently updated, and designed for consumer credit — not SMB lending. Transaction-based underwriting using 12–24 months of payment history is forward-looking, continuously updated, and vertical-specific.

Block/Square’s Square Loans program uses payment data to underwrite small business loans in minutes, with approval rates that would be impossible using traditional credit scoring. The repayment mechanism — a fixed percentage of daily card sales — is also enabled by payment data: Square simply withholds a percentage of each settlement until the loan is repaid. No separate repayment infrastructure required.

For vertical SaaS platforms, the underwriting advantage is amplified by domain knowledge. A property management platform knows that a landlord with 95% rent collection rates and consistent ACH volume is a better credit risk than FICO reflects. A construction platform knows which contractors have consistent project completion patterns. This domain-specific context is impossible to replicate from bank statement data.

5. Dynamic pricing and rate optimization

Advanced platforms are using payment risk profiles to optimize interchange categories and route transactions to minimize processing costs. By intelligently routing based on card type, amount, and merchant risk profile, platforms can recover 3–8 basis points of net take-rate improvement per transaction — meaningful at scale (see Chapter 6 for the optimization mechanics).

For platforms with PayFac economics, dynamic pricing also means tiering merchant processing rates based on risk profile. Lower-risk merchants (longer tenure, lower chargeback rates, consistent volume) can be offered better rates without margin compression; higher-risk merchant categories (fitness/wellness at 0.50%–0.86% chargeback exposure) are priced accordingly.

6. Operational insights and benchmarking

Benchmark data is increasingly valuable to SMB merchants. A restaurant owner wants to know not just their own revenue trend, but how their Tuesday lunch sales compare to similar restaurants in their market. A gym owner wants to know whether their 12% monthly membership churn is above or below industry average.

Aggregate, anonymized payment data across your merchant base allows you to build benchmarking products that no standalone accounting software or bank portal can offer. This creates a “data network effect” — the more merchants on your platform, the better the benchmarks, the more valuable the product, and the harder it is to replicate outside your ecosystem.

Charge Forward Insight

The most common mistake we see in data strategy is launching merchant-facing analytics before securing transaction-level data rights from the processor. Platforms that build a dashboard on a PFaaS feed that only returns aggregated daily totals discover, two years in, that their entire data product is held hostage by the vendor contract. The fix is contractual, not technical. Negotiate raw event-level data access at signing — it is a deal-killer term, and the right vendors will accept it. The Charge Forward Vendor Database flags which PFaaS providers do and do not offer transaction-level data export by default.

Payments as the foundation for embedded finance

Payments is not the destination — it is the starting point. Every embedded finance product that comes after payments is enabled by the data and trust established through the payment relationship. The six-stage progression below reflects the trajectory of the most advanced vertical SaaS platforms globally. (See Chapter 10 for the full strategic roadmap and Chapter 11 for current revenue benchmarks.)

The trajectory is not linear for every platform — the right next product depends on your vertical’s specific needs. A property management platform should pursue banking products (rent escrow accounts, security deposit management) before payroll. A field services platform should pursue lending (equipment financing, line of credit for materials) before insurance. The data you have dictates the product you should build next.

Flagship Advisory Partners estimates embedded finance represents a $500B+ revenue opportunity in North America alone — or $1T+ globally — with fewer than 20% of the market currently addressed. According to the Adyen/BCG Embedded Finance Report (2024), platforms that adopt embedded finance can grow revenues 3–4x their current subscription income, while approximately 80% of the potential market remains untapped.

Case studies in data-driven embedded finance

Block/Square — From payments to $10B+ in loans

Square’s trajectory is the canonical embedded finance playbook. The company began as a payment processing hardware startup (the iconic card reader) and built a merchant data asset of extraordinary depth by processing billions of transactions annually across hundreds of thousands of small businesses.

The insight that became Square Loans: Square could assess the creditworthiness of its merchants with greater precision than any bank, because it had real-time visibility into their daily revenue. The underwriting model uses payment volume, consistency, growth trajectory, and chargeback history to determine loan amounts and approval probability. The repayment mechanism — a fixed percentage of daily card sales withheld at settlement — eliminates the default risk associated with fixed monthly payments (no default if the business has no revenue; the loan simply pauses).

Square Loans has disbursed over $10 billion in financing since launch, with approval times measured in minutes and approval rates that dramatically exceed traditional SBA loan programs. The entire product is impossible without the payment data foundation.

Toast — Payment data → Toast Capital → restaurant intelligence

Toast, which generates the vast majority of its total revenue from financial technology solutions, has used its restaurant payment data to build Toast Capital — a merchant cash advance product targeted at restaurant operators.

The restaurant-specific insight that differentiates Toast Capital: the platform knows not just a restaurant’s total revenue, but the intraday, intraweek, and seasonal distribution of that revenue. A restaurant that does 40% of its annual revenue in November–January is a fundamentally different credit risk in June than in October. Toast’s underwriting incorporates these vertical-specific patterns in ways that bank underwriters, working from monthly bank statements, cannot replicate.

Toast’s path demonstrates the sequential nature of embedded finance maturity: high-attach payments first, then capital products enabled by payment data, then broader financial services anchored by the capital relationship. (See Chapter 11 for current data on Toast’s revenue composition.)

Shopify — The full embedded finance ecosystem

Shopify’s embedded finance evolution is the most comprehensive example in commerce software. Beginning with Shopify Payments (payment processing), the company used transaction data to launch Shopify Capital (merchant cash advances and loans), then Shopify Balance (business banking), and has continued expanding into card issuance, installment payments (Shop Pay Installments), and financial analytics.

The compounding effect: each new product generates additional data that improves the next product. Shopify Balance account holders provide cash flow data that improves Shopify Capital underwriting. Shopify Capital borrowers demonstrate repayment behavior that informs card product risk models. The data flywheel accelerates with each product addition.

Shopify also illustrates the revenue concentration shift that follows embedded finance maturity. As Adyen and BCG note in their joint Embedded Finance Report (2024), first-mover platforms now make more than 50% of their revenue from embedded payments and financial products rather than from software subscriptions. Shopify’s Merchant Solutions segment — which includes payments, capital, and shipping — consistently exceeds its subscription revenue.

Charge Forward Insight

The pattern across Block, Toast, and Shopify is identical: payments first, data second, financial products third. None of them launched lending or banking speculatively — each was built on established payment data that made underwriting, pricing, and risk management tractable. For vertical SaaS platforms evaluating their embedded finance roadmap, the implication is clear: get to high attach rates on payments before adding complexity. Data is not useful at 20% attach; it becomes powerful at 60%+. That is also why the Charge Forward Maturity Framework places lending products at Stage 3 and banking at Stage 4 — sequencing matters.

The competitive moat

Payment data does not just enable new products — it creates structural competitive advantages that compound over time. Three reinforcing mechanisms make a data-driven embedded finance position increasingly difficult to dislodge.

Network effects: more data → better products

Every merchant that joins your payment network contributes to the aggregate data pool that improves your AI models, your benchmarking products, and your underwriting accuracy. A lender with 10,000 merchants in a single vertical can build a credit model that a lender with 500 merchants in the same vertical cannot. The advantage compounds nonlinearly: twice the data does not produce twice the accuracy — it produces models that identify subtler patterns, handle more edge cases, and perform better on the tail of the distribution.

This is why the first platform to reach meaningful payment attach rates in a vertical typically widens its lead over time, not narrows it. Toast’s data advantage in restaurant tech is not just its current position — it is years of proprietary training data that no new entrant can replicate without a decade of market presence.

Switching costs: the financial intelligence problem

A merchant who uses your platform only for scheduling or project management faces moderate switching costs — they need to migrate their data and retrain their staff. A merchant who uses your platform for payments, working capital, business banking, and payroll faces a categorically different switching cost.

Their loan repayment is linked to your settlement process. Their business account holds their operating reserves. Their payroll runs through your system. Their three-year cash flow history lives in your analytics dashboard. Switching software does not just mean data migration — it means rebuilding a financial infrastructure from scratch, potentially during a period of operational disruption. This is the embedded finance switching-cost advantage: customers who cannot leave without financial disruption have materially different retention characteristics than customers who are merely inconvenienced by switching.

First-mover advantage: the data captures the relationship

In any vertical, the platform that first establishes a high-attach payments program will own the payment data advantage — potentially permanently. Historical transaction data cannot be replicated after the fact. A competitor entering the market in year five does not have access to five years of payment history; they start from zero.

This creates an asymmetric window: platforms that act quickly to build payment attach can establish a data position that late movers cannot overcome without either acquiring an incumbent or accepting a permanent disadvantage in model quality and underwriting accuracy. As Flagship Advisory Partners notes in their 2025 Embedded Finance report: “Platforms that miss out on embedded finance will be bypassed by their competitors. It will take hard work and focus to get right, but missing out isn’t an option.”

Building your data strategy

The data advantage described in this chapter is not automatic. It requires deliberate architecture decisions, vendor agreements, and product investments. The five-step framework below is a practical starting point.

The sequencing matters. Platforms that skip Step 1 discover three years later that they have been generating valuable data on behalf of their processor — with no ability to use it for their own AI applications. Platforms that skip Step 2 find that their payment data is analytically inaccessible. Both mistakes are expensive to remedy after the fact.

Charge Forward Insight

The platforms that will dominate their verticals over the next decade are not the ones with the most features or the lowest processing rates. They are the ones that understand their payment data is not a byproduct — it is the product. Every transaction is a data point. Every month of payment history is a more accurate underwriting model. Every merchant that joins your network makes your benchmarking more valuable and your fraud detection more precise. The question your leadership team should be asking is not “how do we add a payments feature?” but “how do we build a financial data engine that compounds in value every month?” That framing changes the product roadmap, the vendor negotiations, the data architecture decisions, and the embedded finance expansion sequence. It also changes the valuation: data-driven embedded finance platforms attract fintech multiples, not software multiples. The market is already rewarding the leaders — Toast at ~85% fintech revenue, Block with a $10B+ lending book, Shopify with a payments and capital ecosystem that dwarfs its subscription revenue. The question for everyone else is whether they move before or after their competitors do.

What’s next

Chapter 9 — “The Migration & Transition Playbook” — begins Part IV, Execution, with a focus on the practical mechanics of moving from one payment vendor to another without breaking the business. For platforms whose data strategy is constrained by their current vendor, Chapter 9 lays out the trigger conditions, the migration paths, the token-vault challenge, and the merchant-communication playbook that separates successful migrations from expensive ones.

For embedded finance revenue benchmarks across verticals, see Chapter 11. To map your platform to the right next embedded finance product, download the Embedded Payments Maturity Framework. To model the revenue impact of expanding from payments into lending, insurance, or banking, run the Charge Forward Payments Revenue Calculator.

SOURCES & REFERENCES

Adyen/BCG Embedded Finance Report (2024); Flagship Advisory Partners — Beyond Payments: The $1T Embedded Finance Opportunity (2025); Flagship Advisory Partners — The Massive Embedded Finance Opportunity SaaS Platforms Cannot Afford to Miss (2025).

Worldpay/Payrix Vertical SaaS Benchmarking Study (January 2025); ServiceTitan FY2026 10-K (March 2026); Toast FY2024 10-K; Block/Square public disclosures; Shopify annual reports.

Charge Forward Embedded Payments Benchmark Report (April 2026); see Chapter 11 for the full benchmark dataset.

Public Charge Forward tools referenced in this chapter: Vendor Database, Maturity Framework, Payments Revenue Calculator. All available at chargeforward.io/tools.

By Jane Podbelskaya · Updated June 20, 2026