The Financial Language of Gender: A Consumer Study Using Machine Learning, Statistical, and Linguistic Analyses

The domain of finance is stereotypically associated with men, and these stereotypes can permeate into the language used by consumers. This study examines whether women and men employ language differently within the finance domain, aiming to better understand the gender gap in consumer finance and inform interventions to mitigate it. Using interdisciplinary approach that integrates machine learning, statistics, and linguistics, we analyzed three distinct language corpora produced by non-expert women and men and centered around 10 key terms relevant to consumer finance. Our analyses revealed notable gender-based differences in language use, manifested in both surface and deep structures of language. These differences were observed in word frequency, metaphor usage, professionalization of language, and conversational strategies, confirming patterns known from previous research in other subject domains. A novel contribution is the identification of a semantic distinction: men’s language more frequently signals agency (active semantic value), whereas women’s language tends to adopt an “experiencer†stance (passive semantic value). We discuss the implications of these findings, emphasizing the need for financial education initiatives that challenge stereotypes and empower both men and women by addressing their distinct financial perspectives and needs.