Modern financial systems generate vast quantities of transactional and event-level data that encode rich economic signals. This paper presents PRAGMA, a family of foundation models for multi-source banking event sequences. Our approach pre-trains a Transformer-based architecture with masked modelling on a large-scale, heterogeneous banking event corpus using a self-supervised objective tailored to the discrete, variable-length nature of financial records. The resulting model supports a wide range of downstream tasks such as credit scoring, fraud detection, and lifetime value prediction: strong performance can be achieved by training a simple linear model on top of the extracted embeddings and can be further improved with lightweight fine-tuning. Through extensive evaluation on downstream tasks, we demonstrate that PRAGMA achieves superior performance across multiple domains directly from raw event sequences, providing a general-purpose representation layer for financial applications.
@article{arxiv.2604.08649,
title = {PRAGMA: Revolut Foundation Model},
author = {Maxim Ostroukhov and Ruslan Mikhailov and Vladimir Iashin and Artem Sokolov and Andrei Akshonov and Vitaly Protasov and Dmitrii Beloborodov and Vince Mullin and Roman Yokunda Enzmann and Georgios Kolovos and Jason Renders and Pavel Nesterov and Anton Repushko},
journal= {arXiv preprint arXiv:2604.08649},
year = {2026}
}