The Worth of a “Wo”: Gender Bias in Financial Advice from LLMs

Richard Foltyn () and Jonna Olsson ()
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Richard Foltyn: Dept. of Economics, Norwegian School of Economics and Business Administration, Postal: NHH, Department of Economics, Helleveien 30, N-5045 Bergen, Norway, https://sites.google.com/view/richardfoltyn
Jonna Olsson: Dept. of Economics, Norwegian School of Economics and Business Administration, Postal: NHH, Department of Economics, Helleveien 30, N-5045 Bergen, Norway, https://sites.google.com/view/jonna-olsson/

No 4/2026, Discussion Paper Series in Economics from Norwegian School of Economics, Department of Economics

Abstract: Do large language models (LLMs) provide gender-neutral financial advice? We answer this question by prompting 33 widely used LLMs from five vendors, varying only a single word in otherwise identical prompts: “man” versus “woman.” We find that women are advised to allocate 1.8 percentage points less to equity funds than men; this gap persists across vendors, model generations, and model complexity. Providing richer investor information attenuates but does not entirely eliminate the gender gap. Since even modest allocation differences imply persistent return differentials, algorithmic financial advice can shape wealth accumulation across demographic groups.

Keywords: Algorithmic bias; Gender bias; Large Language Models; Portfolio allocation (search for similar items in EconPapers)
JEL-codes: C01 G11 J16 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2026-02-27
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