{"ID":2894203,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.10933","arxiv_id":"2507.10933","title":"Artificial Finance: How AI Thinks About Money","abstract":"In this paper, we explore how large language models (LLMs) approach financial decision-making by systematically comparing their responses to those of human participants across the globe. We posed a set of commonly used financial decision-making questions to seven leading LLMs, including five models from the GPT series(GPT-4o, GPT-4.5, o1, o3-mini), Gemini 2.0 Flash, and DeepSeek R1. We then compared their outputs to human responses drawn from a dataset covering 53 nations. Our analysis reveals three main results. First, LLMs generally exhibit a risk-neutral decision-making pattern, favoring choices aligned with expected value calculations when faced with lottery-type questions. Second, when evaluating trade-offs between present and future, LLMs occasionally produce responses that appear inconsistent with normative reasoning. Third, when we examine cross-national similarities, we find that the LLMs' aggregate responses most closely resemble those of participants from Tanzania. These findings contribute to the understanding of how LLMs emulate human-like decision behaviors and highlight potential cultural and training influences embedded within their outputs.","short_abstract":"In this paper, we explore how large language models (LLMs) approach financial decision-making by systematically comparing their responses to those of human participants across the globe. We posed a set of commonly used financial decision-making questions to seven leading LLMs, including five models from the GPT series(...","url_abs":"https://arxiv.org/abs/2507.10933","url_pdf":"https://arxiv.org/pdf/2507.10933v1","authors":"[\"Orhan Erdem\",\"Ragavi Pobbathi Ashok\"]","published":"2025-07-15T02:54:12Z","proceeding":"econ.GN","tasks":"[\"econ.GN\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}