Artificial Finance: How AI Thinks About Money
This research helps understand how LLMs emulate human financial decision-making and reveals cultural/training influences, though it is incremental as it applies existing methods to new data.
The paper systematically compared financial decision-making responses of seven leading LLMs to human responses from 53 nations, finding that LLMs generally exhibit risk-neutral patterns aligned with expected value calculations, occasionally show inconsistencies in temporal trade-offs, and most closely resemble responses from Tanzanian participants.
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.