Structured Foundational Models (ft. Revolut)

​What would it take to build a foundation model for banking?

​Foundation models have transformed language and vision, but applying the same principles to structured financial data presents a very different set of challenges. Unlike text or images, banking data is temporal, heterogeneous, and deeply interconnected, requiring new approaches to representation learning at scale.

​Pavel Nesterov (Head of AI and Data Science, Revolut) and Anton Repushko (Head of Research, Revolut) will share the research behind PRAGMA, Revolut's family of foundation models trained on large-scale banking event sequences. Through the design of PRAGMA, they'll explore the architectural choices, training strategies, and engineering trade-offs involved in building reusable representations for financial data, and discuss how these models can be adapted to downstream tasks such as fraud detection, credit scoring, and customer lifetime value prediction with minimal task-specific fine-tuning.

​Hear how foundation model thinking can extend beyond language, and what it takes to build domain-specific foundation models that generalise across real-world enterprise applications. (Technical Level: 200 - 300)

​More About the Speakers

​Pavel Nesterov is the Executive Director and Head of AI and Data Science at Revolut, where he leads the company's AI strategy, machine learning platforms, and data science organisation. With over 20 years of experience in AI and machine learning, his work spans foundation models, MLOps, AI governance, and large-scale production AI systems. Pavel regularly shares his work at international AI and engineering conferences.

​Anton Repushko is the Head of Research at Revolut Research, where he focuses on foundation models and representation learning for financial data. His research explores large-scale machine learning systems and generative AI, with work spanning financial foundation models and 3D generative modelling.

​More About the Series

​Praxis is a focused technical sharing series built around researcher-driven discussions on advanced AI and applied research topics.

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