This work by the Santander AI lab is designed to help determine which AI model is best suited for a task and deploy it in the most efficient fashion, the bank announced Monday (Oct. 19).
“As part of this effort, Santander has been progressively moving from large general-purpose models towards smaller, specialised models designed for specific banking tasks,” Santander said in a news release provided to PYMNTS.
In selected experiments, this approach has cut serving costs from €5 per interaction to approximately €0.03 while upholding strong performance, the release added.
With the AI lab, Santander has constructed its “Model Arena” to evaluate open-weight models against banking tasks and determine which model is right for each workload. It is also specializing in models using the Nvidia NeMo Customizer.
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The companies say their partnership is already bearing fruit. In one experimental customer-service use case, Santander improved conversation quality while lowering costs by more than 50% by deploying a more efficient AI model.
Now, Santander and Nvidia say they will continue to work together on areas like foundation models, AI agents, fraud and anti-money laundering capabilities, real-time decisioning and advanced simulation technologies, such as banking digital twins.
Aser Blanco, Nvidia’s global banking lead, noted that banking is one of the most data-centric industries, and that turning that data into “proprietary intelligence” about training and fine tuning rather than model size.
“In this new world, cost per intelligence output is a key metric,” said Blanco. “Santander is showing what that looks like in production with smaller models, specialized on the bank’s own data, evaluated against real banking tasks and served efficiently enough to run at the scale of a global bank.”
Recent research by PYMNTS Intelligence shows that 95% of large financial services firms surveyed had widely deployed or embedded newer AI tools in data and technology processes.
“Integration has further to go,” PYMNTS wrote last month, citing additional research showing a 25% adoption rate for AI in API orchestration and integration.
Thirty percent of financial services companies pointed to data quality and fragmentation as their main obstacle to further AI deployment, according to “Financial Services Pulls Ahead in the Enterprise AI Race.”
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