Can AI Help with Your Personal Finances?

2024-12-27

AI Summary

This study systematically evaluates, for the first time, the ability of leading large language models (LLMs)—ChatGPT, Gemini, Claude, and Llama—to deliver accurate financial advice in U.S. personal finance domains: mortgage planning, tax filing, credit management, and investment decision-making. Using a prompt-engineered zero-shot and few-shot question-answering framework, model outputs are rigorously validated against a human-annotated, authoritative financial knowledge benchmark. Results show an average accuracy of 70% across models and tasks—higher in tax and basic lending scenarios, but markedly lower in complex investment guidance. Model version upgrades yield consistent 15–25% accuracy gains. The core contribution is the construction of the first fine-grained, cross-model, cross-domain financial evaluation benchmark, empirically characterizing LLMs’ capability boundaries and evolutionary trends in real-world financial applications. This work provides foundational evidence for the trustworthy deployment of AI in financial services.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Large Language Models
• Personal Finance Management
• Accuracy of Advice

Innovation

• Large Language Models
• Personal Finance Management
• AI Enhanced Financial Advisory