Financial advice behaviour: humans versus AI - Baeckström, Ylva; Matkovskyy, Roman (Journal of Corporate Finance, 2026)
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.