How people judge AI financial advice depends on style and source
Trustworthy FinAInce: Unpacking How AI-Mediated Financial Advice is Judged
Human-Computer InteractionArtificial IntelligenceComputation and LanguageComputers and Society
Summary
Financial advice can come from AI, experts, or online communities, but people trust and rely on each source differently. The authors studied how labeling the advice source and the style of advice affect people's opinions on trustworthiness and safety. They found expert advice is generally preferred and seen as more knowledgeable, even if the advice content is the same. The decision context also changes how risky or safe the advice seems. These insights can help design financial AI tools that encourage careful evaluation rather than blind trust.
What this means in practice
- •For financial technology developers: Create AI financial advice platforms that clearly signal advice style to support users’ accurate trust and reliance decisions.$Commercial implications: Enables development of trustworthy AI financial advisory services that balance user trust and skepticism, improving user engagement and outcomes.
- •For consumer finance advisors: Design client communication strategies incorporating source and style cues to enhance perceived credibility and decision safety.
Authors
Aryan Ramchandra Kapadia, Eshwar Chandrasekharan, Koustuv Saha
Abstract
As generative AI is increasingly used as a source of personal financial guidance, understanding how people appraise such advice is important for supporting appropriate reliance. We conducted a randomized vignette experiment with 285 U.S. adults across eight financial decisions, independently varying three advice styles---AI, expert, and online community---and displayed source labels while holding the underlying recommendation consistent. Advice style most strongly shaped message and safety appraisals, Expert labels selectively increased perceived source knowledge, and decision context primarily shaped risk and safety appraisals. These appraisals were associated with downstream judgments, with models explaining 69.2% of overall quality, 75.9% of trust, and 82.9% of intended reliance. Expert-style advice also remained most preferred when shown without source labels. Our findings have implications for understanding financial advice evaluation, distinguishing the roles of advice style and source labels, and designing financial AI that supports grounded evaluation rather than simply maximizing trust.