Optimizing Financial Dashboard Visualizations Using AI

Abstract

This research investigated how artificial intelligence enhanced financial dashboard visualizations by comparing AI-augmented and traditional approaches across efficiency, usability, and user comprehension. The mixed-methods study involved 10 participants using Kavout (AI-enhanced) and Webull (traditional) platforms through standardized tasks and 10-day diary studies. Results showed AI-enhanced dashboards improved task completion rates by 2% and user confidence through automated recommendations and contextual explanations. However, critical challenges emerged around explainability and trust, with 73% of participants expressing concerns about AI recommendation rationale and bias toward conventional visualizations. The research revealed fundamental tensions between automation efficiency and user control requirements in high-stakes financial environments demanding transparency. Key contributions include empirical evidence for AI's potential to democratize financial analysis, design principles for explainable AI in financial contexts, and frameworks for balancing automation with user agency. Findings suggest future AI-enhanced financial dashboards must prioritize transparent reasoning mechanisms and co-creative human-AI collaboration while maintaining user trust and decision accountability.