Here’s how generative AI could transform the competitive dynamics within banks

By Puja Sharma

Not long ago, in the fall of 2022, generative AI was primarily known to AI engineers and data scientists. Today, this technology has garnered widespread awareness and stands at the forefront of an economic revolution, according to a study by Mastercard. With 55% of the CEOs of large global companies surveyed in 2023 indicating that they are “evaluating or experimenting” with gen AI and 37% that they are already using it, the technology has the potential to unlock trillions of dollars in global economic value.

The report looks at how banks can mitigate the technology’s unique challenges, navigate regulatory uncertainties, anticipate the phased integration from exploration to deployment, and leverage emerging use cases to achieve efficiencies, accelerate productivity and reimagine customer experiences.

Mastercard’s Chief Innovation Officer, Ken Moore said, “We’ve just scratched the surface of potential transformations enabled by generative AI, and expect that within the next year, it will gradually integrate into the operations and products of financial institutions and merchants globally.”

Gen AI poses complex questions around data stewardship for an industry responsible for clients’ most sensitive information. How can banks protect information while using AI to enhance service delivery? What measures can ensure data integrity considering cyber threats and misinformation? Can AI systems serve all customers equitably, free from prejudices that may impact human decision-making? These questions are not merely rhetorical. Banks bear the responsibility of maintaining trust and ensuring compliance in an AI-augmented future. It’s a delicate balance — cultivating AI innovation with one hand while protecting against potential misuse and unintended consequences with the other.

Key trends:

• Knowledge and insights — Bankers equipped with gen AI may find that information searches that once consumed hours could now take minutes. When they need to check up on complex regulations, bankers could, via gen AI, receive cogent summaries — rather than just citations of, or links to, statutes and other raw material.
• Information technology — Gen AI could help with drafting project specifications; writing and debugging code; creating synthetic data with which to stress new solutions’ fraud and risk systems; code refactoring; and more. Day to day, engineers might tap gen AI for stepwise guidance in various tasks.
• Cybersecurity and fraud prevention — Large language models, or LLMs, could be specially tailored for security work. These LLMs could respond to threats and synthesize complex data into clear guidance that professionals can act on. Gen AI’s pattern recognition capabilities could possibly improve the surveillance capabilities of older forms of AI.
• Talent management — With its ability to process unstructured data, gen AI solutions could find and put in front of HR managers candidates who may lack traditional banking employment backgrounds — but have much to offer.
• Client onboarding — Gen AI could streamline know-your-customer compliance and documentation management. Rapidly synthesizing client data, it could flag risks and automate paperwork, expediting time-to-ROI.
• Conversational banking — Gen AI could promise bots capable of responding to customer inquiries in contextually appropriate ways. The image of the bank client trying to bypass a chat system to reach a human operator could become obsolete.
• Wealth advisory — Data-synthesizing gen AI solutions could promise advice unencumbered by emotions or wishful thinking. Financial advisors and their clients could use AI-powered simulations to deepen their grasp of complex investment strategies.
• Credit issuance — Gen AI could reduce loan processing times and associated costs by offering applicants step-by-step conversational guidance. Simpler loan processes could compel more businesses and individuals to apply, spreading the benefits of credit more widely.
• Loyalty programs — Gen AI gives program managers a possible tool with which to communicate with participants about their desires in real time, enabling better matching of people to rewards. Its conversational powers could also guide users through sometimes complicated programs.
• Marketing and communications — Besides using gen AI for dynamic testing and to create emails and social posts, marketers could gain new understanding of consumer reactions by pairing its content generating powers with sentiment analysis and social listening tools.