ARTIFICIAL INTELLIGENCE IN FINANCIAL ANALYSIS

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 (χ²(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 (β = 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: : Artificial Intelligence, Financial Analysis, Trust, Algorithmic Bias, Transparency, Consumer Attitudes