PRAGMA: Revolut Foundation Model
This work provides a general-purpose representation layer for financial applications, enabling strong downstream performance from raw event sequences.
PRAGMA introduces a family of foundation models for multi-source banking event sequences, pre-trained with masked modeling on heterogeneous financial data. The model achieves superior performance on credit scoring, fraud detection, and lifetime value prediction tasks, often with simple linear classifiers.
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.