Revolut is taking a very different approach to AI in banking.
While many financial services companies are using off-the-shelf AI tools or building separate models for fraud, credit risk and customer service, the UK-based digital bank is developing its own foundation model designed specifically for finance. The idea is not simply to apply a language model to financial data, but to teach AI to understand financial behavior itself.
Its proprietary model, PRAGMA, treats transactions, customer interactions, app usage, investments, bill payments and support requests as signals within one connected system. Instead of looking at each banking function in isolation, it learns patterns across the entire customer journey, creating a single AI foundation that can support fraud detection, credit decisions, product recommendations and customer service.
Financial services are quickly becoming one of the most important proving grounds for enterprise AI. The industry has vast amounts of structured data, repetitive processes, complex risk decisions and heavy compliance demands, all of which make it fertile territory for intelligent automation.
Revolut’s approach offers a glimpse of where business AI strategies are heading next. For businesses, the next stage of AI is about moving beyond isolated workflow improvements and creating the foundations for intelligence to build across the entire organization.
A Unified AI For Banking And Finance
As financial services institutions have started working with AI, the usual practice has been to develop separate tools, models and processes for required functions, each requiring its own engineering, training data and maintenance procedures.
Revolut has replaced this stack with a single shared foundation model trained on 40 billion events and interactions with 25 million users.
This means it can learn behavioral patterns across the entire customer journey: app and website use, transactions, trading and investing activity, and payment of bills and subscriptions.
It reports impressive results. PRAGMA has delivered a 64.7 percent improvement in detecting and stopping fraud, a 16 percent increase in credit risk prediction performance, and is 41 percent more effective at recommending relevant products.
On the technical side, the stack is powered by 200 NVIDIA H100 GPUs, which have allowed it to scale from serving 38 million users in 2023 to more than 70 million today.
What sets it apart from competitors using AI for similar tasks is that all of these improvements flow from a single algorithmic and data pipeline. Learnings from one task enable it to improve performance across the board, as models learn in tandem, adapting to shifting economic and behavioral trends.
PRAGMA is the lynchpin of an organization-wide AI strategy that allows the challenger bank to react to change with greater agility than larger incumbent competitors.
This includes its customer service AI assistant, which currently handles 75 percent of support requests without human intervention.
There’s also its first customer-facing agentic AI system, AIR (AI by Revolut; their acronyms are getting better), recently released to UK customers. As well as carrying out routine actions like managing subscriptions or canceling lost cards, it’s designed to be a “lifestyle companion”, assisting with budgeting and even making travel arrangements.
So what lessons can business leaders and professionals take from this and apply to AI strategy in financial services and beyond?
What Can We Learn From Revolut’s AI Strategy?
Many organizations are still running fragmented AI stacks, with different models and data pipelines for specific tasks or functions.
This can quickly become a liability, leading to high maintenance costs and a lack of transparency and best practices within the company. Just as critically, it prevents learning and improvement within one function from driving better performance business-wide.
The Revolut alternative is a unified AI architecture built on a single shared model. This makes it capable of compounding intelligence and the benefits of automated decision-making across the business, rather than simply driving efficiency in isolated tasks.
With billions of data points created daily, the volume and velocity of the data at Revolut’s fingertips mean it is strategically placed to deliver cutting-edge services: Personalized insights, bespoke customer support and agentic tools capable of taking autonomous action on customers’ behalf.
In banking, financial services and beyond, the ability to better understand and react to customer needs is quickly emerging as a massive competitive differentiator.
Taken together, these changes mean that businesses investing in unified AI and data infrastructure are more able to solve problems and pain points, leading to improved customer experience and driving long-term growth.
