Deutsche Bank and NVIDIA have announced a multi-year innovation partnership to accelerate the use of artificial intelligence (AI) and machine learning (ML) in the financial services sector. Deutsche Bank will use NVIDIA AI Enterprise, an all-encompassing software suite that can be used in a data center or the cloud, as part of the agreement, to speed up the creation of services that adhere to regulations. The collaboration will help Deutsche Bank simplify and accelerate its cloud transformation journey.
Christian Sewing, CEO, of Deutsche Bank, said, “AI, ML, and data will be a game changer in banking, and our partnership with NVIDIA is further evidence that we are committed to redefining what is possible for our clients. This partnership is a significant step forward in our AI and ML ambitions. It will help us take a leading position in the usage of these technologies in financial services,” added Bernd Leukert, Deutsche Bank’s Management Board Member responsible for Technology, Data, and Innovation.
With a preliminary focus on three use cases—risk model development, high-performance computing, and the creation of a branded virtual avatar—the companies intend to develop applications across the bank\’s business.
The bank\’s teams have already created an early concept of a 3D virtual avatar to assist employees in navigating internal systems and answering HR-related questions. In the future, immersive experiences with banking clients will be investigated.
Jensen Huang, founder, and CEO, of NVIDIA, said, “Accelerated computing and AI are at a tipping point, and we are bringing them to the world’s enterprises through the cloud. Every aspect of future business will be supercharged with insight and intelligence running at the speed of light. Together with Deutsche Bank, we are modernizing and reimagining the way financial services are operated and delivered.”
A group of sizable language models known as Financial Transformers is also being tested by the companies at this time. These will execute AI and ML models to assist with tasks like detecting data-quality problems, locating early warning signs on the counterparty of a financial transaction, and retrieving data more quickly.