Abstract
Finance is inherently forward-looking, yet financial expectations are necessarily formed from historical and contemporaneous information. Quantitative and qualitative analyses provide the foundation for organizing this information, evaluating risk, and developing expectations about uncertain future outcomes. Financial models, however, remain conditional on assumptions, parameter stability, information availability, and the environments in which they are applied. This paper introduces the concept of data-based intuition as the capacity to form and apply context-sensitive financial judgments that emerge from accumulated analysis of quantitative and qualitative information. The framework does not position intuition as an alternative to analytical rigor; rather, analysis provides the foundation from which informed intuition develops, while intuition contributes to interpreting and applying analytical evidence within particular financial contexts. Data-based intuition is necessarily adaptive and fallible. New information, realized outcomes, and environmental change can reinforce, modify, or invalidate previously learned relationships, requiring both analysis and intuition to remain subject to revision. The framework consequently views financial decision-making as a recursive learning process in which data, analysis, experience, contextual interpretation, decisions, and outcomes continually interact. Artificial intelligence can substantially expand the analytical foundation of this process without eliminating uncertainty about the future applicability of observed relationships. The framework therefore has implications for financial expertise and education as increasingly sophisticated analytical capabilities place greater emphasis on interpretation, adaptation, recognition of limitations, and the development of informed financial judgment.