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

arXiv: https://arxiv.org/abs/2609.20989 · HTML: https://mlsanity.com/paper/2609.20989/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 88 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Builds on (most cited first, 16 of 16)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions](https://mlsanity.com/paper/2107.07015/) (2021)
- [Be Friendly, Not Friends: How LLM Sycophancy Shapes User Trust](https://mlsanity.com/paper/2502.10844/) (2025)
- [Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection](https://mlsanity.com/paper/2401.03737/) (2024)
- [Don't lose the message while paraphrasing: A study on content preserving style transfer](https://mlsanity.com/paper/2308.09055/) (2023)
- [Labeling Messages as AI-Generated Does Not Reduce Their Persuasive Effects](https://mlsanity.com/paper/2504.09865/) (2025)
- [GPT has become financially literate: Insights from financial literacy tests of GPT and a preliminary test of how people use it as a source of advice](https://mlsanity.com/paper/2309.00649/) (2023)
- [Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queries](https://mlsanity.com/paper/2504.09271/) (2025)
- [Do We Know What They Know We Know? Calibrating Student Trust in AI and Human Responses Through Mutual Theory of Mind](https://mlsanity.com/paper/2601.16960/) (2026)
- [Can AI Help with Your Personal Finances?](https://mlsanity.com/paper/2412.19784/) (2024)
- [When AI Gives Advice: Evaluating AI and Human Responses to Online Advice-Seeking for Well-Being](https://mlsanity.com/paper/2512.08937/) (2025)
- [Understanding Trust Toward Human versus AI-generated Health Information through Behavioral and Physiological Sensing](https://mlsanity.com/paper/2512.12348/) (2025)
- [Generating insights about financial asks from Reddit posts and user interactions](https://mlsanity.com/paper/2403.04308/) (2024)
- [Communication styles and reader preferences of LLM- and human-authored COVID-19 information explanations: a case study](https://mlsanity.com/paper/2505.08143/) (2025)
- [How to build trust in answers given by Generative AI for specific, and vague, financial questions](https://mlsanity.com/paper/2408.14593/) (2024)
- [How People Evaluate AI-, Expert-, and Peer-Style Financial Advice](https://mlsanity.com/paper/2608.09019/) (2026)

## Abstract (arXiv)

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.