# Can AI Help with Your Personal Finances?

arXiv: https://arxiv.org/abs/2412.19784 · HTML: https://mlsanity.com/paper/2412.19784/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 27 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [Financial Statement Analysis with Large Language Models](https://mlsanity.com/paper/2407.17866/) (2024)
- [Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction](https://mlsanity.com/paper/2310.05627/) (2023)
- [GPT has become financially literate: Insights from financial literacy tests of GPT and a preliminary test of how people use it as a source of advice](https://mlsanity.com/paper/2309.00649/) (2023)
- [Data Analysis on Credit Card Debt: Rate of Consumption and Impact on Individuals and the US Economy](https://mlsanity.com/paper/2407.11146/) (2024)

## Built on by (most cited first, 4 of 4)

- [The CLEF-2026 FinMMEval Lab: Multilingual and Multimodal Evaluation of Financial AI Systems](https://mlsanity.com/paper/2602.10886/) (2026)
- [Artificial Finance: How AI Thinks About Money](https://mlsanity.com/paper/2507.10933/) (2025)
- [Evaluating Financial Sentiment in the Age of AI](https://mlsanity.com/paper/2609.20198/) (2026)
- [Trustworthy FinAInce: Unpacking How AI-Mediated Financial Advice is Judged](https://mlsanity.com/paper/2609.20989/) (2026)

## Abstract (arXiv)

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.