测试主流AI模型在个人理财中的表现，发现准确率约70%但复杂问题仍不足。
Can AI Help with Your Personal Finances?
- 对比ChatGPT、Gemini等模型在房贷、税务等场景的表现
- 平均准确率约70%，复杂问题回答错误率高
- 新版本模型进步明显，适合普通用户和金融顾问参考
近年来，大语言模型（LLMs）作为人工智能领域的突破性进展，受到产业界与学术界的广泛关注。这些经过海量数据训练的AI系统展现出强大的自然语言处理与内容生成能力。本文探讨了LLMs在解决美国个人财务关键问题上的潜力，评估了OpenAI的ChatGPT、Google的Gemini、Anthropic的Claude及Meta的Llama等主流模型在房贷、税务、贷款和投资等主题上的财务建议准确性。结果显示，尽管这些模型平均准确率约为70%，但在复杂财务查询上仍存在显著局限，且不同主题表现差异较大。然而，分析表明较新版本模型已有明显改进，显示出其在个人与金融顾问场景中日益增强的应用价值。随着技术持续演进，基于AI的个人金融应用前景愈发可观。
原文摘要 · Abstract (English)
In recent years, Large Language Models (LLMs) have emerged as a transformative development in artificial intelligence (AI), drawing significant attention from industry and academia. Trained on vast datasets, these sophisticated AI systems exhibit impressive natural language processing and content generation capabilities. This paper explores the potential of LLMs to address key challenges in personal finance, focusing on the United States. We evaluate several leading LLMs, including OpenAI's ChatGPT, Google's Gemini, Anthropic's Claude, and Meta's Llama, to assess their effectiveness in providing accurate financial advice on topics such as mortgages, taxes, loans, and investments. Our findings show that while these models achieve an average accuracy rate of approximately 70%, they also display notable limitations in certain areas. Specifically, LLMs struggle to provide accurate responses for complex financial queries, with performance varying significantly across different topics. Despite these limitations, the analysis reveals notable improvements in newer versions of these models, highlighting their growing utility for individuals and financial advisors. As these AI systems continue to evolve, their potential for advancing AI-driven applications in personal finance becomes increasingly promising.
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