Authors: Oudom Hean, Utsha Saha, Binita Saha
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.
Source: http://arxiv.org/abs/2412.19784v1
