Generative AI in financial services continues to evolve

The recent AWS Financial Services Symposium was a chance to dive deep into the evolution of generative AI in financial services. In that industry, there’s been a multi-year transformation to the cloud.

But there’s a big mistake that companies can make when thinking about modernizing their technology choices, according to Scott Mullins (pictured), general manager of financial services at Amazon Web Services Inc. It involves thinking of the cloud as a place, or a destination.

Scott Mullins of AWS talks to theCUBE during the AWS Financial Services Symposium.

“It’s binary: We’re either in the cloud or we’re not in the cloud. That is probably the most tried and true way to actually fail at your transformation, because the cloud is not an end,” Mullins said. “It’s a means to the ends that you want to accomplish for your business.”

Mullins spoke with theCUBE analysts John Furrier and Savannah Peterson at the AWS Financial Services Symposium, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the impact of generative AI in financial services and explored how AI is transforming industry practices. (* Disclosure below.)

Business intelligence prompting rethink of financial services

The journey to the cloud is a never-ending one, and the digital transformation continues, according to Mullins. Generative artificial intelligence, meanwhile, represents another set of capabilities on top.

“Think about all the things that we’ve done previously, where it’s like, you don’t have to own your own compute, you don’t have to own your own storage, you don’t have to set up your own networking. Those are the basic building blocks, the fundamentals,” Mullins said.

Now, the question is whether companies even need their developers to have a mastery of COBOL or Python, or whether they can just work with Amazon Q, a gen AI–powered assistant for accelerating software development and leveraging companies’ internal data, according to Mullins. There’s also general consensus that these are still early days for generative AI.

“We talked about the S-curve in the [AWS Financial Services Symposium] keynote, and people thinking that we’re probably in the middle of the S-curve,” Mullins said. “We’re not even at the very beginning of the uptick.”

Amid all of this, data remains a crucial subject of importance. It brings to mind countless conversations around building data lakes and lakehouses. All of these elements are crucial from an organizational perspective, but their true value lies in enabling advanced analytics, according to Mullins.

“We talked a lot about intelligence, and we talked about the definition of intelligence, which is to really be able to actually acquire knowledge and skills. It’s all about, how do I have the capabilities to actually get to intelligence as fast as possible?” he said. “To do that, to actually leverage generative AI to get to intelligence, you’ve got to have your data organized in a way that makes it easy to do that.”

For developers, the goal of something such as Amazon Q is to free people up, according to Mullins. It means doing more critical thinking, rather than writing test scripts, running tests against code and more.

“If you’re not having to go and research, ‘Hey, what should I be doing? Am I using the right language? Am I actually going to get to the right outcome?’ If you don’t have to do any of that and you can just focus on the idea that you are working on and trying to execute and have an assistant in the background doing all that for you, that’s going to free up so many more people to actually be application builders,” Mullins said.

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of the AWS Financial Services Symposium:

(* Disclosure: Amazon Web Services Inc. sponsored this segment of theCUBE. Neither Amazon Web Services nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE