The adoption of artificial intelligence is accelerating at the largest and most forward-looking banks. Leaders have gone beyond establishing responsible AI governance and principles and are broadening the number of use cases; some now have hundreds of them in process.
In 2025, we anticipate attention will pivot to agentic workflows to drive the next level of operational efficiency, coupled with a more disciplined way of measuring returns on investment. Leading banks are shifting from grassroots experimentation with use cases to a bold, top-down AI strategy, identifying ways to responsibly fast-track risk and compliance reviews, and increase impact. We see three broad areas for banking and capital market clients to achieve this:
1) Data as your edge. AI is underscoring the importance of data as a key differentiator, while also bringing to light challenges related to the quality and shareability of data within banking and capital markets firms. Effective AI models need diverse, accurate and large datasets to assess customer risk, prevent financial crimes and develop hyper-personalized products for customer segments. Banks are empowering business and data owners to identify the best use cases by establishing a robust and agile framework for accessing AI tools while maintaining compliance with governance and responsible AI principles.
However, we are seeing data often isn’t clean, collated or organized in a way that can be effectively utilized by multiple business functions. That impairs banks’ ability to surgically automate operations or offer personalized services to help customers manage their finances. Banks should also focus on capturing new types of data to gain a competitive edge by enabling unique and innovative service offerings. While technology already exists to leverage existing data without significant architectural changes, thoughtful approaches will be required to bring down barriers without compromising on information security controls that underpin good identity, access and data management.
“Good” data habits don’t change with AI; however, scale, completeness and accessibility are key to make data “AI ready.” Banks should continue to invest in robust security systems to safeguard sensitive data, enable data accuracy and reinforce governance to manage data assets effectively. Without these safeguards, it’s harder to fully realize the power of AI to enhance end-to-end automation.
2) Race for talent. Moving up the value stack also requires skilled professionals such as data engineers, data architects and AI development experts. We are seeing fierce competition among banks for talent, yet the largest banks are winning simply because they’re so large. Besides a bigger total compensation package, larger firms offer intellectually interesting work on leading programs, such as using digital assets in global commerce. Mid-sized and smaller banks will need to reassess their value proposition and find their niche…
Read More: Banking and capital markets industry trends: PwC
