Artificial Finance: How AI Thinks About Money

TL;DR

This study compares how large language models and humans from 53 nations make financial decisions, revealing that LLMs tend to be risk-neutral and their responses resemble those of Tanzanian participants, with some inconsistencies in future trade-offs.

Contribution

It systematically analyzes LLMs' financial decision-making patterns across diverse cultures, highlighting their risk-neutral tendencies and cultural response similarities.

Findings

LLMs generally exhibit risk-neutral decision-making.

Responses from LLMs most closely resemble Tanzanian participants.

LLMs show inconsistencies in future-oriented trade-off decisions.

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.