With tools comparable to ChatGPT, DALLE-2, and CodeStarter, generative AI has captured the general public imagination in 2023. Unlike past technologies which have come and gone—think metaverse—this latest one looks set to remain. OpenAI’s chatbot, ChatGPT, is maybe the best-known generative AI tool. It reached 100 million monthly energetic users in only two months after launch, surpassing even TikTok and Instagram in adoption speed, becoming the fastest-growing consumer application in history.

In response to 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 a further $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. This can be 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 must 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 remains to be largely nascent. Essentially the most energetic use cases revolve around cutting costs by freeing employees from low-value, repetitive work. Firms 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 economic 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, nonetheless, a spread 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 firms, especially large banks and insurers, still have substantial, aging information technology and data structures, potentially unfit for the use of recent applications. Lately, nonetheless, the issue has eased with widespread digitalization and will proceed to achieve this. 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 firms look like training staff moderately than bidding to recruit from a sparse specialist pool. That said, the problem to find AI talent is already beginning to ebb, a process that will 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, comparable to portfolio evaluation and selection. Firms might want to train their very own models, a process that can 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 review the implications of generative AI more, and historically they’ve rarely approved tools before rollout.