Knowledge that Transforms

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.

Drought can tighten agricultural credit conditions precisely when adaptation investments and access to working capital are most valuable. Using bank balance-sheet data merged with county-level U.S. Drought Monitor data for 2000–2020, we show that local credit markets exposed to drought experience significant declines in agricultural lending, with effects concentrated in severe episodes. These declines are strongest in markets served by geographically concentrated banks, especially single-county institutions, and weaker where lenders are more geographically diversified. In addition, we show that counties with greater irrigation intensity experience smaller lending declines during extreme droughts, while drought-related contractions are concentrated in counties with lower baseline crop resistance. Lending responses are also larger in counties with prior drought experience, consistent with persistent climate risk shaping local credit conditions. Our evidence highlights how climate risk, local adaptation, and bank structure jointly determine the availability of agricultural credit during drought episodes.