# Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomes

arXiv: https://arxiv.org/abs/2307.07422 · HTML: https://mlsanity.com/paper/2307.07422/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 8 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Mathematical Capabilities of ChatGPT](https://mlsanity.com/paper/2301.13867/) (2023)
- [A Systematic Investigation of Commonsense Knowledge in Large Language Models](https://mlsanity.com/paper/2111.00607/) (2021)
- [Protein Language Models and Structure Prediction: Connection and Progression](https://mlsanity.com/paper/2211.16742/) (2022)
- [Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information Access](https://mlsanity.com/paper/2301.10710/) (2023)

## Built on by (most cited first, 11 of 11)

- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [A Complete Survey on LLM-based AI Chatbots](https://mlsanity.com/paper/2406.16937/) (2024)
- [Building Better AI Agents: A Provocation on the Utilisation of Persona in LLM-based Conversational Agents](https://mlsanity.com/paper/2407.11977/) (2024)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [GreedLlama: Performance of Financial Value-Aligned Large Language Models in Moral Reasoning](https://mlsanity.com/paper/2404.02934/) (2024)
- [Advancing and Benchmarking Personalized Tool Invocation for LLMs](https://mlsanity.com/paper/2505.04072/) (2025)
- [Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2506.04290/) (2025)
- [Decoding the Diversity: A Review of the Indic AI Research Landscape](https://mlsanity.com/paper/2406.09559/) (2024)
- [Evaluating Chatbots to Promote Users' Trust -- Practices and Open Problems](https://mlsanity.com/paper/2309.05680/) (2023)
- [A methodology for analyzing financial needs hierarchy from social discussions using LLM](https://mlsanity.com/paper/2602.06431/) (2026)
- [Thin Evidence, Thick Priors: How Language Models Substitute Identity for Missing Financial Facts](https://mlsanity.com/paper/2610.07798/) (2026)

## Abstract (arXiv)

Increasingly powerful Large Language Model (LLM) based chatbots, like ChatGPT and Bard, are becoming available to users that have the potential to revolutionize the quality of decision-making achieved by the public. In this context, we set out to investigate how such systems perform in the personal finance domain, where financial inclusion has been an overarching stated aim of banks for decades. We asked 13 questions representing banking products in personal finance: bank account, credit card, and certificate of deposits and their inter-product interactions, and decisions related to high-value purchases, payment of bank dues, and investment advice, and in different dialects and languages (English, African American Vernacular English, and Telugu). We find that although the outputs of the chatbots are fluent and plausible, there are still critical gaps in providing accurate and reliable financial information using LLM-based chatbots.