Abstract

This paper investigates the impact of algorithmic trust on financial decision-making, specifically focusing on investment choices. We examine the moderating roles of financial literacy and risk aversion in this relationship. Using a mixed-methods approach combining quantitative surveys and qualitative interviews, we analyze how individuals' trust in algorithms influences their investment decisions, considering their levels of financial literacy and risk aversion. Our findings reveal a complex interplay between these factors. While higher algorithmic trust generally correlates with increased adoption of algorithm-driven financial advice, this effect is significantly moderated by financial literacy. Individuals with high financial literacy exhibit a more nuanced approach, calibrating their trust based on the perceived transparency and explainability of the algorithm. Conversely, those with lower financial literacy tend to rely more heavily on algorithmic cues, potentially leading to suboptimal financial outcomes. Risk aversion further complicates the relationship, influencing the type of investment individuals are willing to make based on algorithmic recommendations. This research contributes to the growing body of literature on behavioral finance and fintech, providing insights for policymakers, financial institutions, and algorithm developers seeking to promote responsible and effective use of AI in financial services.