Financial services might be ‘too cautious’ in using gen AI

Despite being a highly regulated industry, financial services is rife with potential generative AI applications.

Speaking at a panel in VB Transform, Shri Santhanam, executive vice president and general manager of Software, Platforms, and AI at Experian North America, said that people think the industry can be slow to adopt technology even though financial services is in the midst of the biggest digital transformation.

“Generative AI is having a profound impact on many industries, so what about financial services?” Santhanam said. “But there are many ways AI can contribute from writing management to productivity, and the biggest transformation is in better financial inclusion.” 

Santhanam and fellow panelist Northwestern Mutual Chief Digital and Information Officer Christian Mitchell pointed to some current use cases for generative AI in the space and how highly regulated industries manage its potential pitfalls. 

Mitchell said one of the ways Northwestern Mutual has been able to use generative AI revolves around client choice and lifecycle analysis. He said AI helps guide Northwestern Mutual clients with the financial and insurance offerings open to them. It also helps bring in lifecycle analysis that gives more insight into retirement options.

Mitchell and Santhanam cautioned against throwing AI into any problem, especially when it might expose sensitive information in an insecure way. However, companies might also be too cautious.

“It’s easy to get stuck in POC (proof of concept) land, so we invested early in production scale and are very careful who to sponsor for go-to-market. We encourage the teams to ideate AI projects, but these have to pass a bar before they can go to market,” Santhanam said.

He added the bar includes strategic alignment, completely custom and better than any off-the-shelf application, and, of course, if it meets specific regulatory requirements. Sanatham said one early experiment in Experian involved a better understanding of its more than 60 years of data, which presented a huge challenge to engineers. 

AI in finance is expected to offer several benefits, such as increased efficiency, cost savings, improved risk management, personalized services and enhanced decision-making. However, many of the use cases so far have revolved around data analysis or customer service, areas with high levels of integration in financial services companies. Analysis from VentureBeat found that some areas of finance remain complex and often require a human employee to manage them.