Mastercard has released Generative AI: The transformation of banking report highlighting 10 key use cases for Generative AI in banking today.

“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.”, said Ken Moore, Chief Innovation Officer at Mastercard.

Fintech Magazine outlined Mastercard’s ten key use cases for Gen AI in banking.

1. 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.  

2. 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.

3. Cybersecurity and fraud prevention

Large language models (LLMs) could be specially tailored for security work. These LLMs could respond to threats and synthesise complex data into clear guidance that professionals can act on.

4. 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.   

5. Client onboarding

Gen AI could streamline know-your-customer compliance and documentation management. Rapidly synthesising client data, it could flag risks and automate paperwork.  

6. 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.  

7. Wealth advisory

Data-synthesising 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.   

8. 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.  

9. Loyalty programmes

Gen AI gives programme managers a possible tool with which to communicate with participants about their desires in real-time, enabling better matching of people to rewards.

10. Marketing and communications

Besides using Gen AI for dynamic testing and to create emails and social posts, marketers could gain a new understanding of consumer reactions by pairing its content-generating powers with sentiment analysis and social listening tools.   

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