Can AI Help with Your Personal Finances?

TL;DR

This paper evaluates the effectiveness of large language models in providing financial advice, highlighting their current capabilities, limitations, and potential for future personal finance applications.

Contribution

It offers a comparative analysis of leading LLMs' performance in personal finance topics, revealing their accuracy levels and areas needing improvement.

Findings

Average accuracy of ~70% in financial advice

Performance varies across different financial topics

Notable improvements in newer LLM versions

Abstract

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.