Financial advice behaviour: humans versus AI

Ylva Baeckström () and Roman Matkovskyy ()
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Roman Matkovskyy: Rennes SB - Rennes School of Business

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Abstract: Financial advice can attenuate underinvestment but is costly, biased, and skewed towards the wealthy. AI-powered co-advisors could help deliver more scalable and affordable advice. To understand how, our vignette-based survey experiment compares the portfolio recommendations made by professional human advisors with GenAI large language models (LLMs) under biased and unbiased prompts. We document human financial advice projection whereby human advisors strongly project their own portfolios onto their clients. AI financial advice projection is prompt and model family dependent: ChatGPT is the least biased, while strong Gemini-Biased projection collapses when removing advisor demographics. LLMs are systematically more conservative than professional human advisors, recommending portfolios with lower Sharpe ratios that deliver up to 18% lower 20-year terminal wealth. However, human advisory fees erode much of this excess gain, with a 20-year breakeven fee of 1.03% p.a. Our results have direct implications for financial regulators, the advice profession, and LLM developers seeking to deploy AI-generated financial advice.

Keywords: Financial advice; Large language models; Artificial intelligence; Portfolio asset allocation (search for similar items in EconPapers)
Date: 2026-09
New Economics Papers: this item is included in nep-ain
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Published in Journal of Corporate Finance, 2026, 101, ⟨10.1016/j.jcorpfin.2026.103044⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05725514

DOI: 10.1016/j.jcorpfin.2026.103044

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