European Financial Services Organizations Adopt Open Models for Proprietary Intelligence

At the recent GTC Berlin conference, leading European financial services organizations showcased their adoption of open models to build proprietary intelligence. This shift towards open models is driven by the need for scalable and efficient AI solutions that can operate at production scale.

Revolut, a prominent digital bank, has rebuilt its technology stack on a single foundation model called PRAGMA, powered by NVIDIA accelerated computing. PRAGMA is designed to handle large volumes of transactions and is engineered to scale 3x, servicing an initial 80 million customers. According to Pavel Nesterov, head of AI at Revolut, ‘PRAGMA answers the question whether foundation models can operate at that scale and across domains.’

Revolut’s PRAGMA model was built in collaboration with NVIDIA and demonstrates the potential for open models to bridge the gap between research breakthroughs and production-ready banking systems. The use of NVIDIA accelerated computing enables PRAGMA to process large amounts of data efficiently, making it an attractive solution for financial institutions.

Starling Bank has developed ‘Starling Intelligence,’ a proprietary intelligence layer that underpins its AI capability. This was achieved by training an agentic model on the bank’s own transaction data using Civo’s U.K. cloud infrastructure. Starling Intelligence is designed to provide actionable insights and support customer processes, minimizing risk and fraud.

Lloyds Banking Group has built ATUM, a transaction foundation model that converts billions of banking transactions into reusable intelligence. Accelerated with NVIDIA RTX PRO 6000 Blackwell GPUs, ATUM improves efficiency across bank-wide use cases. This development highlights the potential for open models to streamline financial operations and enhance decision-making.

SBS has launched a sovereign intelligence platform that enables banks to run generative and agentic models without their data leaving their own walls. This approach addresses concerns around data security and sovereignty, making it an attractive solution for European financial institutions. The use of NVIDIA accelerated computing provides the necessary infrastructure to support these open models.

The adoption of open weight models by European financial services organizations offers several benefits, including proprietary intelligence built on owned data at costs they control. NVIDIA’s accelerated computing platform serves as the engine powering these open models, providing a scalable and efficient solution for large-scale AI applications.

Neobank bunq has integrated its agentic assistant Finn with open weight models powered by NVIDIA accelerated computing. This integration enables Finn to match the quality of leading closed models at 60% of the cost per answer, making it an attractive solution for financial institutions looking to improve customer experience and reduce costs.

NatWest is developing technology designed to recognize British accents, dialects, and financial terminology using NVIDIA’s speech models, including Nemotron 3.5 ASR. This development aims to enhance customer interactions and provide more personalized services.

Modulr is building specialized models for distinct parts of payment operations based on the NVIDIA Nemotron-3-Ultra-550B open model. This approach enables Modulr to develop tailored solutions that meet specific business needs, improving efficiency and reducing costs.

Santander has adopted the new NVIDIA Kumo family of open weight foundation models for structured financial data. These models are used to build specialized intelligence for core banking workloads, enhancing decision-making and operational efficiency.

LSEG is collaborating with NVIDIA to help financial institutions analyze large volumes of historical tick data up to 10x faster using the Polars GPU engine accelerated by NVIDIA cuDF on RTX PRO 6000 Blackwell GPUs. This development aims to provide actionable insights and support investment decisions.

RavenPack has built a dataset generation pipeline that takes plain-language research hypotheses and returns labeled, backtest-ready datasets running on fine-tuned NVIDIA Nemotron models. Investors can use these datasets to identify, analyze, and visualize market trends more efficiently.