How People Evaluate AI-, Expert-, and Peer-Style Financial Advice
2026-08-10 • Human-Computer Interaction
Human-Computer InteractionArtificial IntelligenceComputation and LanguageComputers and SocietySocial and Information Networks
AI summaryⓘ
The authors studied how people judge financial advice that looks the same in content but is presented as coming from different sources: an AI assistant, a certified expert, or an online forum. They found that people generally liked expert advice more than AI advice, even when they didn't know the source. If the source was mislabeled, AI advice was rated better in some ways. The study shows that people's trust in financial advice depends on both who they think it's from and how the advice is communicated, and that simply labeling the source can change how the advice is perceived.
generative AIfinancial advicesource attributioncommunication stylepreregistered experimenttrust in AIexpertise biasvignette studymessage framingmislabeling effects
Authors
Aryan Ramchandra Kapadia, Eshwar Chandrasekharan, Koustuv Saha
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.