Mastercard has outlined key use cases for Generative AI in banking. With banks expected to significantly increase their integration of this technology into frontline and backroom operations over the next year, the new report offers insights into how to navigate the associated challenges and regulatory uncertainties. Here are some of the key applications highlighted in the report:
- Knowledge and insights: Bankers can use Gen AI to deliver cogent summaries of complex regulations quickly, reducing the time-consuming task of searching for information.
- Information technology: Gen AI can help with writing and debugging code, creating synthetic data for fraud and risk system tests, and more.
- Cybersecurity and fraud prevention: Generative AI’s pattern recognition capabilities can enhance the surveillance capabilities of older AI types, and its large language models (LLMs) can provide clear guidance for professionals to act on security threats.
- Talent management: Gen AI can process unstructured data to find potential candidates who may lack traditional banking backgrounds, but have much to offer.
- Client onboarding: Gen AI can streamline the know-your-customer compliance process, quickly synthesise client data, flag risks and automate paperwork, expediting the time-to-ROI.
- Wealth advisory: Gen AI can provide emotion-free, data-driven advice, helping financial advisors and clients understand complex investment strategies more effectively.
- Marketing and communications: Gen AI can be used for dynamic testing and content creation, providing new insights into consumer reactions with sentiment analysis and social listening tools.
The report also highlighted other use cases including credit issuance, loyalty programmes, and conversational banking. The integration of Generative AI into banking operations is set to transform the industry, delivering increased efficiency, productivity, and significantly improved customer experiences.
