In many industries, embracing AI in the application software stack it is not just a matter of training some large language models or recommender systems against general and then specific datasets and plugging it in. And in any regulated industry – particularly financial services because it deals with Other People’s Money – any system that is making decisions about how money changes hands has to be explainable and reproduceable as well as better and quicker than current manual and computerized methods of quantifying risk and moving money around asset classes.

This, in a nutshell, says Gil Perez, chief innovation officer and head of cloud and innovation network at Deutsche Bank, is why we have not seen wide deployment of AI in financial services at the scale we have seen it among the hyperscalers and third party software suppliers and even HPC centers with their giant simulations and models augmented by machine learning.

But the potential benefits for AI in financial services are so large that the multinational investment bank was approach by Nvidia co-founder and chief executive officer, Jensen Huang, 18 months ago to find out how the bank, which is based in Frankfurt, Germany, which has €1.5 trillion in assets under management, and which had €25.4 billion in revenues in 2021, was deploying AI.

But there are hurdles, and many of them are not technical but regulatory, particularly when it comes to data privacy and governance as well as not having an unintended biases.

“We are going to ensure that we have the right frameworks and control environments to ensure that not only are the models are non-biased at the beginning but that they continue to be over time to prevent any kind of drift and to give insight to the regulators,” Perez explains. “So there’s a lot of work that we need to do with that to get the regulators comfortable. We are working with about 46 different regulators, to just so everybody understands the scope, it’s not one or two. It’s not just the Fed