How People Evaluate AI-, Expert-, and Peer-Style Financial Advice

2026-08-09

AI Summary

This study investigates how individuals evaluate financially identical advice when it is attributed to different source labels (accurate, unlabeled, or inaccurate) and communication styles (AI assistant, certified financial planner, or online community). Through a preregistered vignette experiment (N = 285), the authors employ ANOVA and effect size estimation to isolate and quantify the independent and interactive effects of source labeling and communication style while holding content constant. Results indicate that expert-provided advice significantly outperforms AI-generated advice on 8–9 out of 10 evaluation dimensions; however, mislabeling AI advice as expert-originated enhances perceived appropriateness and quality, thereby attenuating the expert advantage. This work provides the first evidence that source labels are not neutral or transparent mechanisms but rather pivotal interpretive frameworks that actively shape trust in financial advice.

Abstract

As generative AI increasingly becomes a common source of daily decision-making, including financial choices, it is critical to understand how people evaluate AI-generated financial advice. We conducted a preregistered vignette experiment (N = 285) in which substantive financial content---including facts, numerical values, recommendation direction, and core reasoning---was held constant while communication style varied across AI Financial Assistant (AI), Certified Financial Planner (Expert), and Online Community Forum (OC) advice. Displayed source attribution was independently manipulated through correctly labeled, unlabeled, and mislabeled conditions, allowing us to separate attribution effects from source-specific communication cues. Expert advice was rated more favorably than AI advice on 9 of 10 outcomes (|d|=0.20--0.47), and this advantage remained visible without source labels, where Expert advice outperformed AI advice on 8 of 10 outcomes (up to d=0.60). Correct labels added limited differentiation, whereas mislabeling increased ratings of AI advice for situational fit and overall quality (d=0.42 for each) and attenuated the Expert advantage in situational fit (d=-0.36). Descriptive analyses further showed that AI advice was most responsive to displayed attribution and, conversely, that advice-style differences were most visible under an AI label. These findings show that financial-advice evaluations are shaped jointly by displayed attribution and message-level communication cues. We position disclosure not as a neutral transparency mechanism, but as an interpretive frame whose accuracy and interaction with message cues can shape trust and reliance.

Problem

• AI-generated financial advice
• expert advice
• peer advice
• source attribution
• communication style

Innovation

• attribution effects
• communication style
• generative AI
• financial advice
• preregistered experiment