The study investigates how people evaluate AI-generated financial advice by conducting a randomized vignette experiment with 285 U.S. adults. Participants were presented with consistent financial recommendations delivered in three styles—AI, expert, and online community—alongside source labels. The results show that advice style most strongly influenced message and safety appraisals, expert labels increased perceived source knowledge, and decision context shaped risk and safety judgments, with these appraisals explaining a large portion of overall quality, trust, and intended reliance.
arXiv:2608. 09019v1 Announce Type: cross 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.
arXiv:2609.24644v1 Announce Type: cross Abstract: As people turn to generative AI for financial advice, these systems can personalize how they communicate and what they say. Whether these forms of pe...
arXiv:2606. 19887v1 Announce Type: cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks.
The study examines how large language models (ChatGPT, Gemini, and Grok) embed religious bias in AI‑generated financial advice. Using 432 simulated advisor‑client interactions across four religious identities and three financial decisions, the authors find that only 12‑18% of advice is unbiased, with Gemini showing the most bias and ChatGPT comparable to Grok. The research identifies structural biases in model design and discursive mechanisms—such as religious anchoring and tone modulation—that vary by scenario, revealing a tension between personalization and neutrality in AI advisory services.
arXiv:2608. 02100v1 Announce Type: cross Abstract: As AI increasingly participates in human decision making, understanding how decision-making authority is distributed between humans and AI has become a fundamental behavioural question.
arXiv:2607. 04103v3 Announce Type: replace-cross Abstract: Generative artificial intelligence is moving from general-purpose experimentation toward specialized applications across banking, capital markets, insurance, payments, and wealth management.
arXiv:2602. 05056v2 Announce Type: replace-cross Abstract: Online scams increasingly leverage fluent and context-aware social engineering strategies, creating growing demand for AI systems that explain why a message may be risky.
Generating actionable financial advice from business records demands that models integrate numerical reasoning, domain knowledge, and sound judgment, while avoiding recommendations that could harm the business. Direct supervision is difficult: historical decisions are not necessarily optimal, and high-quality free-form labels are expensive to obtain.
The paper examines how advisors should tailor recommendations when users consult personal AI assistants whose advice is predictable. It models the influence of personal AI through consultation probability and relative trust, finding that optimal counteraction and loss are hump‑shaped in these dimensions. The study also explores partial predictability, costly adjustments, richer information structures, and presents an online experiment showing participants weigh advisor, personal AI, and their own judgments differently.
arXiv:2608.24842v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as AI analysts to process financial disclosures and support AI-assisted investment decisions....
Demand for personalized financial advising is growing, but consistent advisor expertise is difficult to obtain, scale, and encode in LLM systems. Simple persona prompts rarely specify how a financial advisor should reason and often drift toward generic recommendations.
arXiv:2608. 11787v1 Announce Type: cross Abstract: Generating actionable financial advice from business records demands that models integrate numerical reasoning, domain knowledge, and sound judgment, while avoiding recommendations that could harm the business.