Generative Artificial Intelligence in FinTech Services: Use Cases, Value Creation and Emerging Regulatory Risks

Abstract

The convergence of Artificial Intelligence (AI) and Financial Technology (FinTech) is reshaping the architecture of modern financial services, introducing new capabilities in automation, decision-making, and user interaction. In particular, recent advances in generative AI—most notably Large Language Models (LLMs)—are driving a paradigm shift in how information is produced, communicated, and acted upon within financial institutions. This paper investigates the integration of generative AI into FinTech, focusing on four principal areas of application: automated financial content generation, customer service systems, investment advisory tools, and regulatory compliance reporting. Drawing on recent empirical studies and industry practices, the analysis demonstrates that LLMs are increasingly embedded in financial workflows to enhance speed, scalability, and user interaction. While these systems offer operational advantages, including real-time responsiveness and reduced labour costs, they also introduce substantive challenges related to accuracy, interpretability, and regulatory alignment. The paper evaluates both the functional potential and the structural limitations of generative AI in finance, highlighting the necessity of human oversight, domain-specific fine-tuning, and governance mechanisms. Overall, the findings suggest that generative AI will continue to expand its role in FinTech, particularly through hybrid frameworks that combine automation with expert control.