Revolut just built what might be the most ambitious AI model purpose-built for banking. Called PRAGMA, short for PRe-trained Banking Foundation Model, it’s a series of transformer-based models developed in collaboration with Nvidia that aims to handle fraud detection, credit scoring, and other financial tasks through a single unified system rather than a patchwork of specialized tools.
The numbers behind it are striking. PRAGMA was trained on roughly 40 billion banking events sourced from approximately 25 million Revolut users spread across 111 countries, amounting to 207 billion tokens.
What PRAGMA actually does differently
Traditional fraud detection in banking relies on stacking multiple machine learning models, each trained for a narrow task. One model might flag suspicious transactions, another might assess credit risk, and a third might handle identity verification. Each requires its own feature engineering, its own training pipeline, and its own maintenance.
PRAGMA takes a different approach. It uses masked modeling techniques on tokenized sequences of user interactions, then applies that understanding across multiple tasks simultaneously.
