In finance, generative AI is bulldozing barriers to innovation

GenAI is allowing established banks to innovate at the same speed as fintech

Different financial businesses showcased their uses of generative AI at AWS re:Invent. What stood out was the way that the application of AI is allowing long-standing institutions to innovate at a disruptive pace – provided they invested in data.

The recent AWS re:Invent conference in Las Vegas saw many financial services companies showcasing the platforms and products they had built on the AWS cloud. These companies were all different, in term of services provided and who their customers were, but there were observable threads common to all.

John Kain, Head of Financial Services Market Development at AWS emphasised that a focus on underlying data architecture was key to how successful organisations were likely to be, whether they happened to be a start-up or a long-standing institution. He said:

“There's a lot of commonalities from a technology perspective, in what firms are trying to do. If I had to overly simplify in financial services what the main drivers are, first and foremost is the focus on customer experience, by which I mean being able to automate the process from end to end.

“This means the ability to onboard a customer, be able to support and service them, offer them new products and support them. Almost all of that has been driven over the last few years by artificial intelligence and machine learning and now we’re seeing it in the generative AI space.”

It is intuitive that a financial disruptor like Rocket Companies would have AI at its core. Shawn Malhotra CTO Rocket Companies, speaking at a keynote on the data foundations of AI, explained how Rocket is quite literally built on AI.

“At the heart of it is our patented AI-driven platform,” Malhotra said. “It's the base of being able to build so many of our AI experiences and it’s powered by AWS Bedrock.

“An example of something we built on that platform is our AI powered agent feature that guides home buyers through their home ownership journey. You can chat with that agent and if you do, if you’re like 80% of our clients you’ll prefer that to a phone call, and you'll be three times more likely to close a loan with us. Those are big numbers, and they show you that you can turn buzzword - agent AI, into real value.

“We're helping first-time home buyers navigate that journey, handling 70% of our interactions autonomously. By leveraging our AI platform we've automated tasks like document processing, note taking, sifting through dense mortgage documentation to answering questions and so much more.

“What does that mean for our business and our clients? It's approving first call resolution by 10% and in some parts of our business it's reducing the time required for clients to get their answers by 68%. That’s real value.”

JPMorgan Chase

It’s intuitive that a company like Rocket would build generative AI into its core platforms. JPMorgan Chase is a two-centuries-old institution but Lori Beer, Global CIO, JPMorgan Chase explained to Matt Garman’s keynote audience how this institution is innovating.

“Today, we're actively unlocking Gen AI use cases, and the way we have architected on the cloud has allowed us to modernize our business platforms and build brand new ones, enabling us to continuously innovate,” said Beer.

“We have nearly an exabyte of data, which makes it one of our most critical assets, especially as we increasingly include AI in the way we modernize and build our technology. With the help of AWS data management tools like Glue, our data is discoverable, accessible, interoperable and reusable on our secure end to end data and AI platform. This platform is empowering us to build the next wave of AI applications.

“SageMaker is helping us simplify a model development life cycle from experimentation to model deployment and production. It is the foundation of our firm wide AI platform, which we designed to be repeatable with an extensible architecture so that our data scientists can leverage a range of best-in-class solutions from the ecosystem. Over 5000 employees use SageMaker every month, and we're now starting to explore Bedrock with the goal of providing data scientists with seamless access to more models that can be fine-tuned on our data.”

“Our goal is to use GenAI at scale, and we continue to learn how to best leverage these new innovative capabilities within the regulated enterprise.

“We rolled out our internal AI assistant to about 200,000 employees, and we're already seeing value from some of our other GenAI use cases. Our bankers and advisors receive AI generated ideas to better engage with clients. Our travel agents leverage LLMs to help build trip itineraries for our customers. Our contact centre reps summarise call transcripts, providing insights at scale, and our developers are using AI code generation tools. We're currently exploring ways to leverage developer AI agents in parallel.”

Despite being very different organisations, what both Rocket and JPMorgan Chase have in common is the focus on using GenAI ultimately to provide better experiences for customers which are a result of deploying GenAI to solve genuine business problems.

John Kain elaborates further the commonality not just in the goals of these very different looking businesses, but in the way that insight can be derived from data despite the different application.

“In the insurance space, you see a lot of work around claims processing. We've got used to the idea you can use your phone to take a photograph of an accident, your company will automatically produce an estimate of it and kick off the claims processing. All that's driven by artificial intelligence on the back end.

“But the same technology that you use to process the image for an accident isn't really that different to what Rocket would do to extract information from a mortgage document.

“The ability to get insights from non-structured data are going to look quite similar as you go from of area to area. The applications are a little different what you're trying to do from an end user experience perspective, but there's a lot of similarity in what you're trying to do from an automation and pipeline control through the process.”

A second article tomorrow will examine generative AI innovation from the perspective of different financial services organisations such as Bloomberg and TP ICAP.