The architecture mirrors how large language models learn from text, except the training material is financial behavior. The timing is pointed:
Nvidia’s 2026 State of AI in Financial Services report found 65% of financial institutions already use AI, with nearly 90% deploying or assessing it. The bigger obstacle, it found, isn’t adoption, but the sprawl those efforts created. Most banks have built too many disconnected AI systems, and the fragmentation is now what’s slowing them down.
From Hundreds of Models to One
Instead of building a new AI system every time a new problem arises, banks train one model on all their transaction data and apply it across problems. That shared history changes what the system can see: a payment at midnight looks different when it’s the fourth in ten minutes, from an unfamiliar device, in a city the customer has never bought from before.
Revolut showed what that looks like in practice. In April, the neobank published results from PRAGMA, a model trained on 40 billion transactions across 25 million customers in 111 countries. One system now handles credit decisions, fraud detection and product recommendations that previously required separate ones. Revolut’s head of group credit data science, Tadas Kriščiūnas, said in a statement that the initiative cut the time needed to set up a new use case from weeks or months to “no time required for it at all.”
Mastercard is building toward the same outcome at larger scale. PYMNTS reported in March that the payments network is developing a model trained on billions of anonymized card transactions, including fraud, chargebacks, merchant data and loyalty activity. Personal identifiers are stripped before training begins, so the system works from spending patterns rather than individual records.
Advertisement: Scroll to Continue
Stripe is also rolling out its own AI strategy. According to a May report from TechCrunch, Stripe launched its payments foundation model trained on tens of billions of transactions, raising detection rates for one common type of payment fraud on large businesses from 59% to 97%. Using the Nvidia and AWS platform, Stripe blocked close to $112 billion in fraud last year and cut average fraud rates by 38%, according to a Nvidia blog post on Monday (June 1).
The Business Case Against Fragmentation
The argument for consolidation isn’t just operational. It’s competitive.
Every bank that maintains separate AI models for separate problems pays a compounding cost: each new market requires retraining from scratch, each new use case adds another system to maintain, and none of those systems can use what the others have learned.
Adyen, which processes $1 trillion in payments annually, said in the Nvidia blog post that even a 0.1% improvement in the rate at which payments successfully clear translates to significant incremental revenue for merchants. Shared intelligence makes those gains possible across the board, not just in one product line.
A bank that consolidates its AI into one model trained on its full transaction history can move faster, make better decisions and extend those gains to new problems without starting over.
An Industry Starting Point
Not every institution has Revolut’s data or Stripe’s engineering capacity. Nvidia’s blueprint is designed to give smaller institutions a starting point, letting teams build on their own transaction data without rebuilding systems from scratch.
Services firms including Infosys, EXL and Thoughtworks are helping banks integrate the approach into existing credit, servicing and compliance environments. What Revolut, Stripe, Mastercard and Adyen share is an asset competitors can’t replicate: years of their own customers’ transaction history.
For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.
