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arXiv · 2604.08649

PRAGMA: Revolut Foundation Model

Maxim Ostroukhov·Ruslan Mikhailov·Vladimir Iashin·Artem Sokolov·Andrei Akshonov·Vitaly Protasov·Andrey Goncharov·Dmitrii Beloborodov·Vince Mullin·Roman Yokunda Enzmann·Georgios Kolovos·Jason Renders·Pavel Nesterov·Anton Repushko
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Abstract

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 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.

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Maxim Ostroukhov, Ruslan Mikhailov, Vladimir Iashin, Artem Sokolov, Andrei Akshonov, Vitaly Protasov, Andrey Goncharov, Dmitrii Beloborodov, Vince Mullin, Roman Yokunda Enzmann, Georgios Kolovos, Jason Renders, Pavel Nesterov, Anton Repushko. 2026-04-09. PRAGMA: Revolut Foundation Model. https://arxiv.org/abs/2604.08649

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