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PRAGMA: Revolut Foundation Model

Author

Listed:
  • Maxim Ostroukhov
  • Ruslan Mikhailov
  • Vladimir Iashin
  • Artem Sokolov
  • Andrei Akshonov
  • Vitaly Protasov
  • Dmitrii Beloborodov
  • Vince Mullin
  • Roman Yokunda Enzmann
  • Georgios Kolovos
  • Jason Renders
  • Pavel Nesterov
  • Anton Repushko

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 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.

Suggested Citation

  • Maxim Ostroukhov & Ruslan Mikhailov & Vladimir Iashin & Artem Sokolov & Andrei Akshonov & Vitaly Protasov & Dmitrii Beloborodov & Vince Mullin & Roman Yokunda Enzmann & Georgios Kolovos & Jason Render, 2026. "PRAGMA: Revolut Foundation Model," Papers 2604.08649, arXiv.org.
    • Handle: RePEc:arx:papers:2604.08649
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    References listed on IDEAS

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    1. Shijie Wu & Ozan Irsoy & Steven Lu & Vadim Dabravolski & Mark Dredze & Sebastian Gehrmann & Prabhanjan Kambadur & David Rosenberg & Gideon Mann, 2023. "BloombergGPT: A Large Language Model for Finance," Papers 2303.17564, arXiv.org, revised Dec 2023.
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