Santander builds AI capabilities that improve performance and lower costs by more than 50% using NVIDIA AI infrastructure
In an experimental customer-service use case, Santander improved conversation quality while reducing costs by more than 50%. Santander establishes a three-year framework for the next phase of collaboration with NVIDIA.
Banco Santander announced a new milestone in their artificial intelligence collaboration with NVIDIA. “With support from NVIDIA, the Santander AI Lab has developed new capabilities to evaluate AI models and agents for banking applications, helping identify which model is best suited for each task and deploy it in the most efficient way.” – according to the press release.
The work is being carried out through Santander AI Lab, which explores and tests emerging AI technologies before broader deployment across the bank. “As part of this effort, Santander has been progressively moving from large general-purpose models towards smaller, specialised models designed for specific banking tasks. In selected experimental use cases, this approach has reduced serving costs from around €5 per interaction to approximately €0.03 while maintaining strong performance.” – the bank said.
Through the Lab, Santander has built Model Arena to evaluate open-weight models against banking tasks and identify the right model for each workload, and is specialising models using NVIDIA NeMo Customizer. “Customer-service comparisons delivered evidence that smaller, specialised models can deliver strong performance on targeted banking tasks. Santander has built specialised intelligence for core banking workloads on NVIDIA Kumo, a family of open-weight foundation models for structured data, allowing companies to quickly turn structured data into predictive insights needed for agentic services.” – the bank explained.
The Lab is industrialising inference within Santander’s own architecture using NVIDIA Dynamo-Triton, the approach behind the serving-cost reduction above, and is developing Model Arena to design, simulate, evaluate and supervise banking agents, alongside performance engineering and adversarial testing of conversational AI in controlled environments. The goal is to establish a rigorous experimentation path through which emerging AI technologies are optimised and stress-tested against banking requirements before successful capabilities scale across Santander.
Building on these advances, Santander is progressing from open-weight models towards specialised models and governed banking agents designed for greater control, safety and lower operating costs at scale. The collaboration is already delivering measurable results: in an experimental customer-service use case, Santander improved conversation quality while reducing costs by more than 50% through the deployment of a more efficient AI model.
As part of the next phase of the initiative, Santander and NVIDIA have established a three-year collaboration framework covering areas such as foundation models, AI agents, fraud and anti-money laundering capabilities, real-time decisioning and advanced simulation technologies, including a banking digital twin.
Iñaki Bernal, chief artificial intelligence officer at Santander, said: “NVIDIA is a key player in the infrastructure, software and research that is powering the latest advances in artificial intelligence. Combined with Santander’s scale across businesses, customers and geographies, we believe we are uniquely positioned to succeed in transforming those advances into practical banking capabilities.”
Aser Blanco, Global Banking Lead at NVIDIA, said: “Banking is one of the most data-centric industries. Data sits at the heart of every process and now banks need to become intelligence-centric. Transforming years of data into usable and proprietary intelligence is not about model size, but about training and fine tuning. In this new world, cost per intelligence output is a key metric. Santander is showing what that looks like in production with smaller models, specialised on the bank’s own data, evaluated against real banking tasks and served efficiently enough to run at the scale of a global bank.”
