Why Platforms Matter More Than Models in Deploying AI for Payments

Artificial intelligence has arrived in payments. Big Tech players are embedding AI into authentication, personalisation, cashierless checkout and conversational commerce.

Lately, Amazon attributed its 35% profit surge to its AI investments in payments and checkout.

Fintechs are also experimenting aggressively with agentic AI, real-time recommendations, and automated customer service.

bunq’s AI assistant “Finn”, part of Europe’s first AI-powered neobank, now handles up to 40% of user support questions independently while assisting with up to 75% of queries daily.

Yet for many tier-1 and tier-2 banks, processors, and established fintechs, the question is not whether to use AI, but how to do so without compromising scale, security, or regulatory compliance.

What prevents AI adoption in payments

Most financial institutions face three fundamental obstacles on their AI journey: a lack of clear AI strategy, a weak core technology and data backbone, and operating models built for a slower era.

While strategy and talent matter, AI initiatives consistently stall at the same bottleneck: high-quality data. Payments data is complex, sensitive, and highly transactional.

You cannot simply “add AI” to a legacy platform and expect results. AI requires clean, structured, real-time data.

Many AI use cases require systems that can interpret AI outputs and execute actions instantly.

AI in payments is about acting while a transaction is happening, not generating insights after the fact.

An AI agent delivers value only if the system can respond in real time: authorising, routing, updating limits, triggering customer interactions, or adapting the payment flow.

Way4 and Way4 DMP: clean data meets AI-ready, real-time core

This is where OpenWay’s Way4, a digital payments software platform trusted by leading banks and fintechs worldwide, becomes decisive.

Way4 was designed as a real-time financial core, capable of sharing live data and executing actions online.

With this foundation, the Way4 Data Management Platform (DMP) enables institutions to treat AI as an API service, embedded directly into payment flows.

Successful AI in payments depends on where data is created and how quickly it can drive action.

Way4’s real-time payments core authorises and executes transactions at scale, generating clean, structured, and context-rich data when decisions are made.

Way4 DMP transforms this real-time data into AI-ready structures, enabling institutions to analyse behavior, experiment rapidly, and deploy AI-driven logic inside live payment flows, not in disconnected systems.

Together, Way4 and Way4 DMP allow organisations to move from AI pilots to production quickly and safely, enabling real-time interpretation and action while maintaining enterprise control. Institutions choose between three flexible models:

• Data as a Service – Real-time, structured payment data for AI use cases
• Model Training – Using Way4 data to train AI models tailored to business goals
• Train-and-Deploy Agent Services – Deploying AI agents that operate directly within payment processes

AI capabilities shift from theoretical to operational, embedding intelligence into payments and enabling experimentation, scale, and measurable outcomes.

Cloud-first by design, enterprise by nature

Way4 DMP is built on a cloud-first architecture designed specifically for fintech and digital payments.

It delivers elastic scalability, rapid deployment, and continuous innovation without disrupting operations.

Container orchestration, CI/CD pipelines, infrastructure-as-code, and advanced observability tools enable fast iteration, automated resilience, and efficient scaling of real-time data pipelines.

Crucially, Way4 DMP is not a generic data platform. It is natively aware of Way4’s data models, transaction semantics, and execution logic, and interacts with the Way4 payments core in real time.

This tight integration allows data to be captured, analysed, and acted upon within the same transaction lifecycle, supporting live decisioning, experimentation, and AI-driven logic inside payment flows.

At the same time, the architecture respects enterprise realities.

Data can remain local where sovereignty or regulatory requirements demand it, combining cloud-native agility with the governance and reliability expected of a core payments platform.

AI in payments is about experimentation at speed

AI is inherently experimental. For banks and processors, the challenge is enabling this experimentation without disrupting production systems or incurring excessive costs.

This is where AI-empowered platforms become essential as technological sandboxes for rapid innovation.

When experimentation is built into the platform, AI projects become affordable, measurable, and repeatable.

Pay-as-you-go economics further allow organisations to calculate the ROI of each use case precisely, creating confidence to move from pilot to production.

From AI vision to commercial reality

Institutions that win in the AI era will treat AI as a continuous capability, not a one-off project, embedding intelligence directly into payment flows and scaling what works.

With Way4 DMP, OpenWay helps banks, fintechs, and processors move beyond isolated pilots.

Through a focused workshop, teams can align on core principles and identify high-impact use cases, then shape and launch an MVP on real payment data, scaling proven AI capabilities safely across the payment business.

Featured image: Edited by Fintech News Philippines, based on image by aleksandr_samochernyi via Freepik

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