The adoption of generative AI technologies in the financial sector is transforming operational processes, decision-making, and customer interactions. While these innovations enhance efficiency, they also raise a critical question: how can financial institutions balance automation with the value of human expertise? This study proposes a novel framework categorizing applications of generative AI in finance along two dimensions: the degree of automation and the value added by human intervention. The framework, developed through a comprehensive literature review, is validated with case studies in areas such as portfolio management, compliance, and risk assessment. It categorizes applications into four quadrants, balancing low and high levels of automation and human expertise. The findings highlight the potential of hybrid models (Quadrant 4), where advanced automation is combined with human oversight, offering the greatest efficiency and accuracy. For instance, a fintech company implementing AI-driven compliance tools with human supervision enhanced error detection in regulatory filings while maintaining compliance standards. This research provides a structured framework for integrating generative AI into financial workflows, helping institutions optimize the balance between automation and human expertise. It offers practical insights for decision-makers and serves as a foundation for responsible AI adoption, ensuring operational efficiency and strategic soundness in an era of digital transformation.