Artificial Intelligence – How Different Sources of Financial Advice (AI vs Human) Impacts Decision Disparities in Personal Finance

Sammanfattning

Abstract: The objective of this study is to examine the conditions under which financial recommendations – originating from either artificial intelligence (AI) or human advisors – contribute to increases or decreases in individual financial welfare, as well as to identify the heterogeneous effects across different types of individuals. The empirical analysis is based on an online survey experiment conducted via Prolific and implemented in Qualtrics, in which participants responded to nine financial decision-making tasks alongside a battery of measures capturing financial literacy, financial ignorance, and financial self-efficacy. The final sample consists of 1,581 observations, which are analyzed using a mixed-effects linear probability model incorporating question-level fixed effects and standard errors clustered at the respondent level. The findings indicate that financial welfare outcomes are significantly influenced by the quality of the recommendations provided, rather than by their source (AI versus human). Moreover, individual differences in financial literacy, financial self-efficacy, and financial ignorance systematically affect the extent to which recommendation quality translates into welfare outcomes. The results further provide robust evidence on the detrimental impacts of low-quality financial advice, thereby reinforcing existing literature on the adverse consequences of poor recommendations. Overall, the study concludes that, irrespective of whether individuals exhibit greater trust in AI-based or human-generated advice, the primary determinant of financial welfare is the intrinsic quality of the recommendations. This underscores the critical importance of advisory quality in shaping financial outcomes, highlighting that the effectiveness of financial guidance depends less on its origin and more on its informational and analytical rigor.