Trustworthy FinAInce: Unpacking How AI-Mediated Financial Advice is Judged

arXiv:2609.20989v1 Announce Type: cross 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.

Study on AI financial advice

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   e63624d9-26ab-42ed-b4d6-3a5076805f57
           https://arxiv.org/abs/2609.20989
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EVIDENCE   3 extracted excerpts
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MODEL RUN  1 run, methodology 1.0.1
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SCORE      0  (Mixed or uncertain)
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CONFIDENCE 70%