SambaNova Systems announces SambaNova GPT Banking

By Edlyn Cardoza

SambaNova Systems, the company delivering the industry’s comprehensive software, hardware, and solutions platform to run AI and Deep Learning applications, announces SambaNova GPT Banking, a solution purpose-built for the financial services industry, enabling banks to create clear market separation in AI by jump-starting their deep learning language capabilities in weeks, not years.

“GPT Banking has the potential to transform nearly every aspect of banking, from improving operations to managing risk and compliance,” states Rodrigo Liang, CEO and co-founder of SambaNova. “Customers tell me they’re most excited about GPT Banking’s ability to truly personalize the customer experience, with richer insights that enable them to understand customers’ evolving needs.”

There is a deep learning deployment gap in banking resulting in financial services companies not being able to build and deploy AI models fast enough to meet their business needs — SambaNova GPT Banking has solved that problem: “On average, it takes banks 18 months to hire a skilled data science team, build the infrastructure, train and deploy a large language model. By the time they are ready to deploy, the institution’s model is out of date — as the base model size will have increased by 10X within the year and the compute requirements will have changed dramatically,” stated Marshall Choy, SVP of Product at SambaNova Systems. “

“We built GPT Banking to improve bank’s competitiveness and efficiency while accelerating their digital transformation,” stated Liang. “AI is the quickest and most cost-efficient tool to do that today and our service can be deployed and delivering value in weeks.”

GPT Banking is built for banks’ large language models and is offered as a subscription service to simplify deploying the most advanced language models in a fraction of the time.

Banks can leverage the technology to perform:

• Sentiment analysis: scan social media, press and blogs to understand the market, investor, and stakeholder sentiment.
• Entity recognition: reduce human error, classify documents, and reduce manual/repetitive work.
• Language generation: process, transcribe and prioritize claims, extract necessary information, and create documents to improve customer satisfaction.
• Language translation: language translation to expand customer base.