The current private equity (PE) value-creation playbook is under pressure. Buyouts that once required only about 5% annual EBITDA growth to hit target returns now take roughly 12%. (see Figure 1).

Written in collaboration with

Written in collaboration with

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Bain & Company’s 2026 Global Private Equity Report attributes this 2.5x increase to tougher deal economics, including higher entry multiples, reduced leverage, and stagnant multiple expansion. Beyond these factors, most operating partners ignore a substantial value pool: the financial infrastructure layer inside their portfolio companies.

Figure 1

PE-backed software and SaaS companies enable payments for business-to-business (B2B) and business-to-consumer (B2C) customers across verticals like hospitality, wellness, and e-commerce, as well as marketplace procurement and supplier payouts. Currently, they sit on top of high-volume payments flows, recurring cash flows, and rich transaction data—without capturing its value.

In Europe alone, PE-owned software platforms touch around €0.9 trillion in payment flows (Bain estimate based on S&P, UBS, Worldpay). Yet Bain analysis has found that fewer than 10% of companies have a payments monetization strategy in their value-creation plan. That gap is also particularly acute in Europe, where a fragmented software landscape and lower card fee pools have slowed the monetization shift already well underway in the US.

For an exemplary portfolio company in a high-frequency consumer vertical with €50 million in annual recurring revenue (ARR) and more than €500 million in payment volume, that uncaptured revenue could total €2.5 million to €5 million annually. That’s incremental EBITDA with a minimal cost base, high customer stickiness, and compounding returns over the hold period.

As commercial models shift from per-seat licensing to usage- and consumption-based pricing (a trend accelerated by agentic AI workloads paid per application programming interface (API) call, token, or task completed), revenue becomes more variable and harder to forecast. It also becomes more difficult to grow through traditional levers alone. Under this new structure, adjacent, high-margin revenue layers built on top of existing payment flows become a critical source of value.

The embedded finance premium

Sophisticated software investors are already moving in this direction, dedicating operating capabilities to payment monetization and fintech integration. Funds that delay risk missing upside and facing a valuation gap at exit. Buyers are increasingly accounting for embedded finance maturity when pricing and underwriting software assets.

The data bears this out. Platforms with embedded payments trade at higher enterprise-value-to-revenue than software-only platforms. Meanwhile, platforms that combine payments, treasury, and lending command even higher valuations (William Blair, “How Embedded Finance Drives Enterprise Value and Increases Multiples for SaaS Platforms,” October 2025). While causality is difficult to isolate, the directional signal from proprietary mergers and acquisitions transaction data is consistent across deal cycles.

Three value-creation levers PE operators should prioritize

Three actions can help PE operators unlock material incremental EBITDA without acquiring new customers or expending significant capital:

1. Capturing economics from current payments volume 

The most immediate opportunity is capturing economics on payment volume already flowing through the platform. Software companies often facilitate millions of dollars in annual transactions but earn nothing from that flow. Simply switching from a referral arrangement to an embedded model allows the platform to own the payment experience and retain a net margin, substantially increasing incremental annual revenue.

Even at the early stages of this transition, where platforms typically capture 20 to 30 basis points (bps) net, a company facilitating €500 million in annual transactions could gain €1.0 million to €1.5 million in incremental revenue without increasing volume. Platforms that progress further along the ownership spectrum, moving from third-party referral models to full payment facilitation, can capture materially more value.

Previously, the infrastructure required to embed payments took years to build. Today, it can be spun up quickly with immediate returns. Based on industry benchmarks, platforms that adopt embedded payment components with pre-built compliance and onboarding report significant increases in customer lifetime value, higher retention, and a meaningful shift in revenue mix toward payments (Stripe).

Phorest, an Ireland-based software company serving spas and salons, saw a 335% increase in payments revenue by unifying online and in-person payments into a single embedded platform, while a unified onboarding reduced salon set-up time to under two minutes.

Dines, an all-in-one hospitality platform in the UK, increased revenue by owning its payment stack rather than outsourcing it. By unifying online ordering, tableside payments, and in-person checkouts, Dines drove a 150% increase in revenue per venue and is on track to quadruple its year-over-year transaction volume.

The monetization lever also extends to metering and billing, matching the market’s pricing shift toward usage- and consumption-based models. Capitalizing on this allows firms to recover 10% to 12% of recurring revenue that would otherwise silently slip away. Stripe’s acquisition of Metronome, the billing engine behind OpenAI, Anthropic, and Nvidia, signals how central this capability has become.

2. Automating financial operations 

The second opportunity targets the cost side of the profit and loss (P&L). Most PE-backed software companies still run financial operations manually—for example, reconciling settlement files in spreadsheets, maintaining static payout schedules, or spending days each month closing payment activity. These processes are both slow and error-prone. Manual reconciliation across multiple payment providers and bank accounts allows mismatches to slip through and compound silently.

Unreconciled transactions, delayed exception handling, and reporting gaps inevitably surface at month-end—or worse, during due diligence. Infrastructure APIs that provide real-time settlement and automated reconciliation remove this risk.

This opportunity has two dimensions:

• On the revenue side: Embedded treasury capabilities, such as yield-bearing financial accounts and automated cash sweeping, generate new high-margin revenue streams from previously idle balances. Incorporating financial products into a vertical SaaS offering can increase average revenue per user (ARPU) by 2–5x while lowering customer acquisition cost.  
   
• On the cost side: Automating reconciliation, settlement reporting, and compliance workflows can reduce financial close cycles by  40% to 60% and improve data accuracy by up to 90%. It eliminates some of the most error-prone manual steps in the finance function.

Glofox, an Irish SaaS platform for gyms and fitness studios, automated its financial operations to transform onboarding and franchisee payments. Previously, franchise customers waited six weeks to receive their first payment; now they go live within hours. Automated billing retry and recovery processes also reduced payment declines by 21% and disputes by 10%.

Similarly, Aureus Academy, a music education network in Singapore, transitioned from manual payment processing to a fully automated financial infrastructure. As a result, it cut reconciliation time by 90%. About 95% of invoices are now paid at the start of each month, and it can roll out new locations twice as fast.

3. Cutting fraud losses while increasing authorization rates

AI-enabled fraud is rising rapidly, exposing software companies to greater fraud losses, chargebacks, and false declines that erode revenue and customer trust. Every euro of fraud ultimately costs European merchants €4 once chargebacks, operational overhead, and lost goods are factored in (LexisNexis, “True Cost of Fraud” study, 2025 edition). Without network-level fraud detection, firms absorb these costs silently, often without understanding the full P&L impact on their value creation plans.

Modern machine-learning-based fraud models are trained on trillions of dollars in payments, enabling them to detect patterns that would be invisible to individual companies. With network-level detection, companies can reduce fraud rates by more than 30% while improving legitimate approval rates. For high-volume platforms, lower fraud losses and higher authorization rates flow directly to the bottom line.

Risk infrastructure and revenue optimization can be the same investment, not competing ones. VisualSoft, an e-commerce platform in the UK, achieved a 98% authorization rate after consolidating its fragmented payment stack, while its retail partners saw conversion rates increase up to 35%.

Dermalogica, a global skincare company, saw fraud rates drop 50% after implementing AI-powered fraud detection, significantly reducing chargebacks and manual review times. 

First-party fraud, such as trial abuse, multi-account attacks, and usage-based billing failures, is an emerging threat that erodes margin without appearing on traditional fraud dashboards. According to data from Stripe, 14% of new account registrations at AI and SaaS companies involve suspected multi-account abuse.

For PE operators, fraud infrastructure doubles as a growth enabler. When a platform blocks abuse at sign-up, it can safely onboard legitimate accounts faster (Stripe, “Analyzing First-Party Fraud Trends,” Stripe blog, 2026). Rapid and secure onboarding enhances a platform’s reputation and market presence, creating a competitive advantage that benefits both the platform and its sub-merchants.

Seizing the opportunity

These actions help PE operators grow revenue, widen margins, and strengthen their risk and resilience postures. The magnitude of impact depends on platform-specific factors, but even conservative upside delivers compounding returns. Consider the potential across three primary levers:

• Revenue expansion: This is typically the largest and most variable lever. Platforms with high payment-to-software-revenue ratios, such as restaurants and retail, have the most to gain by moving to embedded models. Revenue upside ranges between 20 to 30 bps for companies with embedded payments, compared to just 5 to 15 bps for those relying on traditional referrals. In addition, platforms stand to regain 1% to 2% of ARR by reducing involuntary billing churn and recovering failed payments.
  
  
• Margin improvement: This scales alongside two factors: the volume of customer funds on the platform (treasury yield) and the footprint of manual finance operations. Platforms with high daily balances (e.g., marketplaces and event ticketing platforms) and large back-office teams often see the highest returns from embedded treasury features, with ARPU increasing by up to 15%. Meanwhile, operating costs can be reduced by 3% to 5% by automating reconciliation, settlement, and compliance workflows.
  
  
• Risk and resilience: These postures are heavily affected by fraud exposure and transaction geography. Verticals with high chargeback rates or significant cross-border volume benefit most. On average, 0.5% to 1.5% of platform payment volume can be recovered by reducing chargebacks, false declines, and operational fraud management costs. Authorization optimization is often a larger but less visible benefit; firms can see a 1% to 4% revenue lift by improving first-attempt approval rates. 

The table below illustrates the financial impact of reclaiming trapped payments value, using the above-mentioned representative platform with €50 million in ARR and €500 million in annual payment volume.

Revenue capture opportunity	Potential EBITDA upside (annual range)	EBITDA translation
Payment monetization	€1.0M–€1.5M	€500M × 0.2%–0.3% in net revenue; no incremental cost to serve
Billing recovery	€0.5M–€1.0M	€50M × 1%–2% in recovered payments; no acquisition or delivery costs for existing customers
Treasury and operational automation	€0.5M–€1.5M	Yield earned on idle customer balances combined with 3%–5% savings on finance operations cost base
Fraud avoidance and authorization gains	€0.5M–€1.0M	20%–40% recovery of gross fraud costs
Authorization optimization	€0.1M–€0.2M	1%–4% authorization uplift (retaining 20–30 bps on unlocked volume)
Cumulative potential upside	€2.6M–€5.2M

Revenue capture opportunity	Potential EBITDA upside (annual range)	EBITDA translation
Payment monetization	€1.0M–€1.5M	€500M × 0.2%–0.3% in net revenue; no incremental cost to serve
Billing recovery	€0.5M–€1.0M	€50M × 1%–2% in recovered payments; no acquisition or delivery costs for existing customers
Treasury and operational automation	€0.5M–€1.5M	Yield earned on idle customer balances combined with 3%–5% savings on finance operations cost base
Fraud avoidance and authorization gains	€0.5M–€1.0M	20%–40% recovery of gross fraud costs
Authorization optimization	€0.1M–€0.2M	1%–4% authorization uplift (retaining 20–30 bps on unlocked volume)
Cumulative potential upside	€2.6M–€5.2M

Combined, the incremental EBITDA opportunity ranges from €2.6 million to €5.2 million. At a 15x to 20x EBITDA multiple, that incremental margin translates to €39 million to €104 million in enterprise value—a massive difference over a five-year hold. Importantly, this value is extracted entirely from existing customers and payment flows without requiring new logo acquisitions, incremental sales headcount, or significant capital expenditure.

Realizing the upper end of this range requires more than choosing the right infrastructure partner. Portfolio companies must build foundational capabilities in-house, particularly around how to sell, price, and support payments services. Integration, operational setup, and compliance readiness all require up-front effort and costs that should be explicitly budgeted for in the activation business case.

Timingwise, platforms typically begin generating incremental revenue within the first year of activation, with financial returns compounding in subsequent quarters as front-book penetration deepens. Over time, back-book conversion follows.

More mature portfolio companies can find additional upside in next-horizon plays. Once core infrastructure is live, platforms can naturally extend into embedded lending, working capital solutions, and real-time financial dashboards that drive customer retention and upsell opportunities.

Agentic AI as a force multiplier

Agentic AI transforms infrastructure upgrades into self-improving systems. Unlike earlier waves of automation, agentic AI systems work autonomously across multiple steps, adapt in real time, and learn from past outcomes. In practice, a reconciliation agent doesn’t just flag an anomaly; it researches the source, drafts a resolution, routes the exception for approval, and posts the entry.

This capability is not a separate value lever; it’s a value multiplier. The same transaction data that feeds payment routing can simultaneously sharpen fraud models and tighten cash forecasts, making the system smarter with every transaction.

This is not a future-state concept. Strategic Treasurer’s 2026 “AI in Treasury & Finance Survey” and 2026 Treasury Technology Analyst Report found that 22% of companies are already deploying agentic AI in treasury and finance operations. Financial infrastructure APIs reveal payment routing, cash operations, and fraud signals as programmable layers that agentic systems can act on directly. Card networks have launched dedicated agentic commerce tokens, and leading platforms are actively connecting AI frameworks to live payment orchestration.

Early movers are seeing measurable returns:

• Wise’s routing intelligence agent dynamically selects the optimal payment path across its global network, delivering 74% of transfers in less than 20 seconds (Wise, “Q4 Mission Update: Speed,” Wise blog).
  
  
• Lifetime Products deployed an AI reconciliation agent to autonomously match and post accounts receivable deductions, achieving a 95% efficiency improvement in its first quarter (Microsoft, “Lifetime Products Rebalances Workforce with Autonomous Agents in Dynamics 365,” Microsoft Customer Stories).
  
  
• In compliance applications, multi-agent systems are processing millions of regulated tax and legal cases autonomously, allowing companies to replace or reallocate headcount.

PE-backed software companies are ideal environments for agentic deployment due to their high transaction volumes, cross-system dataflows, exception-heavy processes, and rich transaction histories. For PE investors, the compounding logic is clear: Each core financial lever generates rich transaction data, including payment flows, reconciliation patterns, fraud signals, and cash positions. Agentic AI feeds on that data to continuously optimize routing decisions, tighten cash forecasts, and drive down exception rates. The longer a platform operates with agentic infrastructure, the wider the performance gap grows compared to competitors still running manual or rules-based operations. Agentic AI doesn’t create one-time efficiency gains; it sets off a compounding flywheel.

Five questions for every operating team

How much of this value is accessible to a portfolio company, and how quickly? Five diagnostic areas reliably expose high-potential candidates. To uncover their upside, operating teams should ask:

1. What share of transaction volume flows through the platform? How much does it capture, and does the financial infrastructure prevent revenue leakage?

The gap between current and achievable revenue capture is typically a platform’s single largest value-creation opportunity. To calculate the upside potential, operators must quantify three variables: total platform-facilitated payment volume, the current monetization model (referral, integrated, or embedded), and the net take rate retained. Operators must also assess whether the current billing infrastructure allows silent leakage to occur via failed transactions or involuntary customer churn.

2. Where is cash sitting, and is it working? 

Platforms with meaningful float have an immediate treasury yield opportunity; those without can still benefit from embedded financial accounts as a customer retention play. To evaluate the opportunity, map average daily balances held on the platform or in transit against settlement timing and determine whether customers use outside banking tools that the platform could replace.

3. How manual are financial operations? 

High manual intensity signals both a near-term automation opportunity and readiness for agentic AI because the data flows needed to train agents are typically already present. Calculate the full-time equivalent headcount and other costs dedicated to reconciliation, settlement reporting, you’re your customer/anti-money laundering, and chargeback management to find the potential value recovery.

4. Is the data infrastructure ready for agentic AI?

Agentic deployments require financial data—such as payment events, reconciliation outcomes, fraud signals, and cash positions—to be available in real time, structured consistently, and accessible via API. Platforms on modern, API-first infrastructure can typically move to pilot deployment within months; those on legacy batch integrations may need to be migrated first.

5. Does the organization have the capability to execute? 

Embedded finance requires deep expertise in fintech and compliance infrastructure, as well as commercial capabilities that most software companies lack in-house. The choice here is often build vs. partner. To decide, evaluate whether the portfolio company has the regulatory and technical foundations required to build or if partnering with an infrastructure provider is the faster path to capturing the economics.

These diagnostic questions are effective across various portfolio company archetypes, including vertical SaaS with high-frequency payments, B2B marketplaces with complex multiparty settlements, and supplier platforms with payout-driven retention.

Operators can explore two to three priority portfolio companies in a structured sprint, resulting in a financial flow map with lever-by-lever sizing and a prioritized activation roadmap. This approach provides investors with a concrete business case for each asset rather than a generic thesis.

Building a repeatable value-creation playbook

This paper focuses on how software companies can extract more value from their payments systems—an opportunity that is often ignored or underleveraged. Unlocking this value creates a durable revenue stream regardless of how broader market shifts affect software defensibility. Payments monetization is best pursued alongside software modernization, not as a substitute for it. In fact, a modernized, AI-enabled stack makes embedded finance faster to deploy and harder for competitors to replicate.

Funds with significant software exposure should build payments and financial infrastructure expertise directly into their operating teams, making it a standing capability akin to commercial excellence or technology diligence. PE firms that treat embedded finance as a repeatable, portfolio-wide playbook, rather than a one-off initiative, will compound this advantage across every software investment they make.

About Stripe

Stripe is a technology company that builds economic infrastructure for the internet. Businesses of every size—from new startups to public companies—use our software to accept payments and manage their businesses online.