📄 Abstract
This study examines the role of artificial intelligence in financial analysis through a comprehensive survey of 240 respondents across various age groups and educational backgrounds. Using chi-square and regression analyses, the research investigates relationships between demographic factors, AI understanding, and attitudes toward AI financial applications. Key findings reveal a significant association between age group and trust in AI financial systems (?²(20) = 44.9, p = 0.001, Cramer's V = 0.216), with middle-aged respondents (45-54 years) demonstrating the highest trust levels. Additionally, AI understanding significantly predicts comfort with AI analyzing personal financial data (ß = 0.167, p = 0.016), though the effect is modest (R² = 0.0240). The study identifies investment management (24.6%) and fraud detection (22.9%) as the areas most improved by AI, while revealing substantial concerns regarding algorithmic bias (65.8% moderately to extremely concerned) and data security (only 4.6% expressing complete trust). These insights provide valuable guidance for financial institutions implementing AI solutions and highlight the importance of transparency and education in fostering AI adoption.
🏷️ Keywords
📚 How to Cite:
Sudeep Tholar, Sowmya D.S , ARTIFICIAL INTELLIGENCE IN FINANCIAL ANALYSIS , Volume 12 , Issue 6, june 2025, EPRA International Journal of Economics, Business and Management Studies (EBMS) , DOI: https://doi.org/10.36713/epra22818