In accordance with a McKinsey report, generative AI could add $2.6 trillion to $4.4 trillion annually in value to the worldwide economy. The banking industry was highlighted as amongst sectors that would see the most important impact (as a percentage of their revenues) from generative AI. The technology “could deliver value equal to an extra $200 billion to $340 billion annually if the use cases were fully implemented,” says the report. 

For businesses from every sector, the present challenge is to separate the hype that accompanies any recent technology from the actual and lasting value it might bring. It is a pressing issue for firms in financial services. The industry’s already extensive—and growing—use of digital tools makes it particularly prone to be affected by technology advances. This MIT Technology Review Insights report examines the early impact of generative AI inside the financial sector, where it’s beginning to be applied, and the barriers that have to be overcome in the long term for its successful deployment. 

The essential findings of this report are as follows:

• Corporate deployment of generative AI in financial services continues to be largely nascent. Probably the most energetic use cases revolve around cutting costs by freeing employees from low-value, repetitive work. Corporations have begun deploying generative AI tools to automate time-consuming, tedious jobs, which previously required humans to evaluate unstructured information.

• There’s extensive experimentation on potentially more disruptive tools, but signs of business deployment remain rare. Academics and banks are examining how generative AI could assist in impactful areas including asset selection, improved simulations, and higher understanding of asset correlation and tail risk—the probability that the asset performs far below or far above its average past performance. Thus far, nevertheless, a variety of practical and regulatory challenges are impeding their industrial use.

• Legacy technology and talent shortages may slow adoption of generative AI tools, but only temporarily. Many financial services corporations, especially large banks and insurers, still have substantial, aging information technology and data structures, potentially unfit for the use of recent applications. Lately, nevertheless, the issue has eased with widespread digitalization and should proceed to accomplish that. As is the case with any recent technology, talent with expertise specifically in generative AI is briefly supply across the economy. For now, financial services corporations look like training staff relatively than bidding to recruit from a sparse specialist pool. That said, the issue to find AI talent is already beginning to ebb, a process that might mirror those seen with the rise of cloud and other recent technologies.

• Harder to beat could also be weaknesses within the technology itself and regulatory hurdles to its rollout for certain tasks. General, off-the-shelf tools are unlikely to adequately perform complex, specific tasks, akin to portfolio evaluation and selection. Corporations might want to train their very own models, a process that may require substantial time and investment. Once such software is complete, its output could also be problematic. The risks of bias and lack of accountability in AI are well-known. Finding ways to validate complex output from generative AI has yet to see success. Authorities acknowledge that they need to check the implications of generative AI more, and historically they’ve rarely approved tools before rollout.