Shifting Patterns and Evolving User Sentiments Regarding Artificial Intelligence in Financial Decision Making

Abstract

This research studies the impact of user perceptions on trust and acceptance of artificial intelligence in financial decision-making. A structured questionnaire was administered to capture user sentiments regarding challenges and opportunities associated with AI in finance. Statistical techniques that is ANOVA, paired sample t-tests, and the Tukey Honestly Significant Difference (HSD) test, were performed to explore the data and assess the significant differences in user attitudes. The results supported the alternative hypothesis, revealing that user perceptions significantly impact their trust and acceptance of AI-driven financial solutions. ANOVA analysis demonstrated varying levels of trust and acceptance among different user groups, while paired sample t-tests highlighted significant disparities in concerns about security and privacy, trust in AI, and satisfaction with transparency. Further, key findings suggest that users who perceive more challenges or fewer opportunities tend to have lower trust in AI for financial decisions. Moreover, key differences were found across different demographic groups that highlights the need for customer-focused methods in the development of AI. The implications for financial institutions underscore the importance of addressing user concerns to enhance trust and acceptance of AI-driven solutions. This study contributes to the growing body of research on AI adoption in finance and provides opportunities for future research into longitudinal trends and user-centric AI development strategies. Keywords: artificial intelligence, finance, user perceptions, trust, acceptance, ANOVA, paired sample t-tests, Tukey HSD test, customer-focused methods, AI adoption.