The future of AI in banking

The future of AI in banking

Management teams with early success in scaling gen AI have started with a strategic view of where gen AI, AI, and advanced analytics more broadly could play a role in their business. This view can cover everything from highly transformative business model changes to more tactical economic improvements based on niche productivity initiatives. For example, leaders at a wealth management firm recognized the potential for gen AI to change how to deliver advice to clients, and how it could influence the wider industry cash flow statement template for excel ecosystem of operating platforms, relationships, partnerships, and economics.

AI in banking: strategic investments and navigating trends

The good news here is that more than half of each financial services respondent segment are already undertaking training for employees to use AI in their jobs. While these skills are often necessary in the initial stages of the AI journey, starters and followers should take note of the skill shortages identified by frontrunners, which could help them prepare for expanding their own initiatives. Frontrunners surveyed highlighted a shortage of specialized skill sets required for building and rolling out AI implementations—namely, software developers and user experience designers (figure 13). That said, what differentiated frontrunners (figure 7) is the fact that more leading respondents are measuring and tracking metrics pertaining to revenue enhancement (60 percent) and customer experience (47 percent) for their AI projects.

Common traits of frontrunners in the artificial intelligence race

There are multiple options for companies to adopt and utilize AI in transformation projects, which generally need to be customized based on the scale, talent, and technology capability of each organization. Capabilities such as foundation models, cloud infrastructure, and MLOps platforms are at risk of becoming commoditized, given how rapidly open-source alternatives are developing. Making purposeful decisions with an explicit strategy (for example, about where value will really be created) is a hallmark of successful scale efforts.

A checklist of essential decisions to consider

1. Data leaders also must consider the implications of security risks with the new technology—and be prepared to move quickly in response to regulations.
2. GenAI models such as GPT, with its transformer architecture, mark a quantum leap from the AI of yesteryear, which primarily focused on understanding and processing information.
3. As the technology matures, the pendulum will likely swing toward a more federated approach, but so far, centralization has brought the best results.
4. Dive into the data compiled from a survey of over 400 financial services professionals—including executives, data scientists, developers, engineers, and IT specialists—from around the world.
5. As we harness its capabilities, we pave the way for a financial sector that is not only more efficient and effective but also more just and responsive to the needs of a rapidly changing world.
6. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.

The insurance sector benefits from more efficient claims processing and risk assessments, as revealed during the EY collaboration with a Nordic insurance company to use AI in automating repetitive tasks in the claims process. The solution streamlined document processing, allowing agents to focus on more complex tasks and improving overall efficiency and customer satisfaction. Once companies start implementing AI initiatives, a mechanism for measuring and tracking the efficacy of each AI access method could be evaluated.

Banks are responding by implementing robust data security measures, anonymizing data where feasible, and securing explicit customer consent to AI use. Adherence to stringent data privacy regulations such as GDPR is a cornerstone of these efforts, ensuring responsible stewardship of customer information. Learn about Deloitte’s offerings, people, and culture as a global provider of audit, assurance, consulting, financial advisory, risk advisory, tax, and related services. Ltd., is a research specialist at the Deloitte Center for Financial Services where he covers the insurance sector.

This comprehensive approach ensures that the adoption of AI in banking is not only technologically innovative but also ethically responsible and aligned with the long-term interests of customers and the broader financial ecosystem. The scalability of AI solutions and their integration with existing legacy systems are vital considerations for banks aiming to future-proof their services. This includes developing talent, managing AI capabilities, and ensuring AI-driven decisions are transparent and justifiable. The banking sector’s commitment to the continuous learning and updating of AI models is crucial in adapting to new data and evolving market conditions. As financial services companies advance in their AI journey, they will likely face a number of risks and challenges in adopting and integrating these technologies across the organization. Our survey found that frontrunners were more concerned about the risks of AI (figure 10) than other groups.

Much has been written (including by us) about gen AI in financial services and other sectors, so it is useful to step back for a moment to identify six main takeaways from a hectic year. With gen AI shifting so fast from novelty to mainstream preoccupation, it’s critical to avoid the missteps that can slow you down or potentially derail your efforts altogether. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States and their respective affiliates.