The Trade-off Between Data Privacy and Personalization in the Financial Sector: Can Generative AI be a moderator in Gen Z’s Investment Decisions?

The increasing digitization of financial services has amplified the trade-off between personalized offerings and data privacy. While tailored investment recommendations provide significant advantages to investors, especially those that are Generation Z individuals (GZIs), concerns over data security, fraud and potential misuse persist. In parallel, it accelerates the tertiarization of the sector, shifting the focus from traditional banking institutions towards an AI-driven financial ecosystem. This study explores the role of Generative AI (GAI) as a moderator in this balance, analyzing how it can facilitate investment decisions without compromising privacy. Through qualitative interviews with ten American GZIs (five males, five females), the findings suggest that while this cohort is generally inclined to share personal data for personalization, financial decision-making presents unique challenges, such as the need for human interaction in finalizing financial decisions and different feelings regarding traditional and digital banks. GAI emerges as a key enabler, providing investment insights through synthetic data generation and federated learning, ensuring both personalization and privacy. The study contributes to financial technology research by highlighting GAI’s potential in reshaping investment advisory services.