PRAGMA: Revolut Foundation Model
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 f...
| Authors | Maxim Ostroukhov, Ruslan Mikhailov, Vladimir Iashin et al. |
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| Primary category | cs.LG |
| Published | 9 April 2026 |
| Last updated at source | 24 August 2026 |
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