They can also have difficulty going deep enough on a single gen AI project to achieve a significant breakthrough. It can be difficult to implement uses of gen AI across various business units, and different units can have varying levels of functional development on gen AI. With this archetype, it is easy to get buy-in from the business units and functions, as gen AI strategies bubble from the bottom up. It can slow execution of the gen AI team’s use of the technology because input and sign-off from the business units is required before going ahead.
Future-proofing through scalability and integration
Generative AI (GenAI) opens the way for innovation and operational efficiency in the financial services sector. As we embrace the vast potential of artificial intelligence (AI), it is crucial to navigate its inherent challenges responsibly. The focus extends beyond merely implementing technology — it involves cultivating an ecosystem that is ethically sound, transparent and inclusive. As financial institutions invest in strategic AI integration, they are not just keeping pace with advancements, but driving them forward. Harnessing AI paves the way for a promising banking future, ready to meet the demands of a rapidly changing world. The second factor is that scaling gen AI complicates an operating dynamic that had been nearly resolved for most financial social security and medicare 2020 institutions.
- Ltd., is a research specialist at the Deloitte Center for Financial Services where he covers the insurance sector.
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- Companies can also look at making best-in-class and respected internal services available to external clients for commercial use.
- This portfolio approach likely enabled frontrunners to accelerate the development of AI solutions through options such as AI-as-a-service and automated machine learning.
Capturing the full value of generative AI in banking
Banks can use the data to simulate how customers might respond to these new products or to other scenarios, like a financial recession. Some FS firms are already trialing tools in this space, but it may take some time before they are truly enterprise ready. We recently conducted a review of gen AI use by 16 of the largest financial institutions across Europe and the United States, collectively representing nearly $26 trillion in assets. Our review showed that more than 50 percent of the businesses studied have adopted a more centrally led organization for gen AI, even in cases where their usual setup for data and analytics is relatively decentralized.
Frontrunners seem to have realized that it does not matter how good the insights generated from AI are if they do not lead to any executive action. A good user experience can get executives to take action by integrating the often irrational aspect of human behavior into the design element. However, the survey found that frontrunners (and even followers, to some extent) were acquiring or developing AI in multiple ways (figure 9)—what we refer to as the portfolio approach. From our survey, it was no surprise to see that most respondents, across all segments, acquired AI through enterprise software that embedded intelligent capabilities (figure 9).
Companies would need time to gather the requisite experience about the benefits and challenges of each method and find the right balance for AI implementation. We found that companies could be divided into three clusters based on the number of full AI implementations and the financial return achieved from them (figure 1). Each of these clusters represents respondents at different phases of their current AI journey. Financial services are entering the artificial intelligence arena and are at varying stages of incorporating it into their long-term organizational strategies.
Robotic process automation in financial services
Naturally, banks encounter distinct regulatory oversight, concerning issues such as model interpretability and unbiased decision making, that must be comprehensively tackled before scaling any application. Banks that foster integration between technical talent and business leaders are more likely to develop scalable gen AI solutions that create measurable value. Many fintechs will play an enabling role by helping to democratize gen AI’s capabilities for mid-market and smaller financial institutions, allowing these firms to leverage gen AI in a way that currently is only available to the largest FS players in the world.
Data leaders also must consider the implications of security risks with the new technology—and be prepared to move quickly in response to regulations. As the technology advances, banks might find it beneficial to adopt a more federated approach for specific functions, allowing individual domains to identify and prioritize activities according to their needs. Institutions must reflect on why their current operational structure struggles to seamlessly integrate such innovative capabilities and why the task requires exceptional effort. The most successful banks have thrived not by launching isolated initiatives, but by equipping their existing teams with the required resources and embracing the necessary skills, talent, and processes that gen AI demands. While implementing and scaling up gen AI capabilities can present complex challenges in areas including model tuning and data quality, the process can be easier and more straightforward than a traditional AI project of similar scope. Finally, scaling up gen AI has unique talent-related challenges, whose magnitude will depend greatly on a bank’s talent base.