Banks are under a regulatory avalanche, and it is making talk of innovation difficult — with one rather stark exception: artificial intelligence. Despite all of the consent orders and hushed voices of the past year over “hot” topics, everyone still seems keen to talk about AI. It is a little ironic given this area is likely one of the most unchartered of all for many executives. But it seems too omnipresent to ignore.
According to CCG Catalyst’s New Frontiers Survey 2024, which asked C-level bank executives in the US about their attitudes and priorities, AI is likely to be most impactful to the industry, poses the greatest opportunity for their bank, and comes with the most risk across a range of innovative areas asked about. That made it pretty much all-consuming across the survey results, signaling this technology and its applications in financial services are very much top of mind for bank executives today.
But what does that mean? When executives are thinking about AI and talking about AI, what exactly are they talking about and thinking about? Unfortunately, the answer likely is, they don’t know. As Maya Mikhailov, CEO and founder of SAVVI AI, said in an interview, many tend to look for use cases for AI rather than starting with the problem they are trying to solve. Instead, she suggests, bank leaders should think of AI as a set of tools that can be applied to achieve different solutions. “The faster they understand that AI is a toolkit, the faster they will be able to fit the right tool to the right problem,” she said.
The problems that AI can solve are generally data or content heavy, depending on the kind of AI you are talking about. Machine learning is very good for identifying patterns in datasets and making predictions based on those patterns, for example, while generative AI is useful for documents you need to summarize, analyzing conversations, or if you want to create or pull together content quickly, Mikhailov explained.
Today, some of the more common applications for machine learning in banking include improving anti-money laundering efforts, fraud detection and prevention, underwriting, analytics, biometrics, and automated document processing, per CCG Catalyst’s report. Generative AI, meanwhile, is being used to improve chatbot functionality and self-service tools. It also offers efficiency plays, such as summarizing customer service interactions or creating personalized marketing content.
The goal, though, should not be to try to apply AI to all of these things. Instead, executives should take a step back and think about what they are trying to achieve. Most of the problems financial institutions have today are typical of banking — they need to grow; they need to drive efficiency. The next step is exploring how to approach those things and developing a strategy around them.
If in building that broader strategy, you come across a data problem, that is when it is time to talk about AI. It is not before that, and it is certainly not at every board meeting. It is also likely that, at least for now, most FIs’ AI strategies will leverage primarily established technologies like machine learning for any kind of decisioning. That is because generative AI models lack the ability to explain their outputs and still vary widely in their ability to replicate results, even under those same conditions. In this sense, we are not even really talking about anything particularly groundbreaking — yet.
So, the key takeaway? The buzz is not where it’s at. AI, especially newer forms, has a lot of promise. But it will take time for that promise to evolve into something tangible. In the meantime (and always, really), banks should be approaching this technology with a practical mindset. As Bahadir Yilmaz, chief analytics officer of ING, told CNBC recently, “It’s a really powerful tool. It’s very disruptive. But we don’t necessarily have to say we are putting it as a sauce on all the food.”
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