Putting a price on fintech innovation: How to rethink AI monetisation

Fintech has always been a fast-moving industry. Now, artificial intelligence is accelerating that pace even further, reshaping how fintech products are built, delivered, and scaled. But while AI is unlocking new value in the finance sector, monetising AI innovation remains a major challenge.

Companies like OpenAI (ChatGPT), Microsoft Copilot, and Claude by Anthropic rely on monthly or annual subscriptions to provide premium access to their AI services. While these models offer predictable revenue, they often fall short in dynamic environments because they don’t necessarily account for the complexity and unpredictability of scaling AI. From fluctuating compute demands to unclear customer willingness to pay based on usage or outcomes, monetising AI remains a largely uncharted territory. Even the most established players are navigating unknowns around pricing, profitability, and the long-term economics of delivering AI-powered products at scale.

As fintechs seek to capture value from AI-driven products, many are rethinking traditional pricing models. New pricing models, particularly in cloud-based financial services and data analytics, are gaining traction, offering a flexible approach to unlocking AI’s commercial potential. And even companies not yet developing AI-powered solutions are recognising that strategic pricing innovation is becoming a competitive necessity, with market dynamics compelling businesses to evolve their approach or risk falling behind.

New standards in pricing

One emerging approach is usage-based billing (UBB), which links cost to actual usage and resource consumption. This model is particularly well suited to AI products and cloud-based services, where costs and value can vary widely depending on how intensively a tool is used. For instance, AI translation service DeepL charges based on the volume of text translated, while content generation platform Jasper bills according to the amount of content produced.

While UBB is an important piece of the puzzle, it's not a cure-all. Other models – like flat fees for simplicity, recurring subscriptions for predictability, and outcome-based pricing that ties cost to measurable results – each offer unique advantages. For most companies, the most effective strategy isn’t choosing between one model over another. It’s taking a hybrid approach.

Today, 43% of companies mix subscription with usage-based pricing, while 8% combine subscription with outcome-based approaches. Only 16% remain purely subscription-based, and less than one in 10 rely on consumption (9%) or outcome-based (8%) only. It’s clear: hybrid monetisation has become the norm, not the exception.

The results speak for themselves. 67% of companies using hybrid pricing models reported profit margin improvements – more than double the rate seen with usage-based models alone (32%). The implication is clear. Blending models doesn’t just offer flexibility, it boosts financial performance.

Why hybrid works for AI

In the AI space, where customer usage can swing dramatically depending on compute loads or user uptake, this flexibility is essential. A fixed monthly fee risks undercharging high-volume users or overcharging light-use customers, neither of which supports long-term retention or profitability.

Usage-based billing helps address this gap, but it can create unpredictability for customers, making budgeting difficult and potentially reducing adoption. By layering subscriptions with usage-based components, companies can provide stability without sacrificing scalability. This is particularly critical as financial institutions embed AI deeper into their core operations and demand more transparency into ROI.

Similarly, outcome-based pricing – tying costs to results such as questions answered or fraud detection improvements – offers another compelling element of a hybrid approach. Instead of paying for AI access or usage, customers pay for proven impact. Combined with a base subscription, this model gives organisations predictable baseline costs while only paying more when the system proves its value.

Experimentation is key — but so is infrastructure

The shift toward hybrid models isn’t without friction. Implementing hybrid models requires billing systems that can handle multiple pricing dimensions while maintaining accurate customer records, compliance, and revenue recognition. Many businesses simply aren’t set up for this kind of complexity.

What’s more, speed becomes critical in this environment. While 83% of companies test pricing before making changes, those who can act within a month of testing are significantly more likely to see success. The biggest barriers to rapid iteration are metering gaps, usage model complexity, and technical limitations—challenges that modern billing infrastructure is designed to solve.

Companies with modern, flexible monetisation tools are better positioned to iterate, optimise, and scale. Those relying on legacy systems or manual processes risk being left behind – not because their products lack value, but because their billing systems can’t adapt fast enough to capture market opportunities.

Futureproofing monetisation for the AI era

As AI continues to evolve, pricing strategies must adapt. Effective fintech monetisation requires a flexible approach that goes beyond a single pricing model. Successful businesses create systems that can seamlessly integrate multiple pricing structures – subscription, usage, outcomes, and one-off models – all carefully designed to align with customer behaviour and strategic business objectives.

For fintechs, hybrid pricing models are more than just a go-to-market tactic; they're a growth engine. With the right infrastructure and mindset, these models can unlock sustainable, value-aligned revenue, even in the most complex and rapidly evolving environments.

By embracing this approach, businesses can future-proof their monetisation and prepare to lead in a future where pricing is as dynamic and intelligent as the technologies it supports. In an era where innovation is reaching historic heights, embracing dynamic pricing isn't just advantageous. It's essential for capturing opportunity, sustaining growth, and staying ahead, whatever the next innovation brings.