# Are Generative AI Agents Effective Personalized Financial Advisors? arXiv: https://arxiv.org/abs/2504.05862 ยท HTML: https://mlsanity.com/paper/2504.05862/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. - Builds on 10 of its 50 references that are in the map. - First posted on arXiv 8 Apr 2025. ## Placed in - LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/) ## Builds on (most cited first, 10 of 10) - [NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark](https://mlsanity.com/paper/2310.18018/) (2023) - [Character-LLM: A Trainable Agent for Role-Playing](https://mlsanity.com/paper/2310.10158/) (2023) - [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023) - [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020) - [Conversational Recommender System](https://mlsanity.com/paper/1806.03277/) (2018) - [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024) - [Emotional Intelligence of Large Language Models](https://mlsanity.com/paper/2307.09042/) (2023) - [Conversational Information Seeking](https://mlsanity.com/paper/2201.08808/) (2022) - [An Exploratory Study of Stock Price Movements from Earnings Calls](https://mlsanity.com/paper/2203.12460/) (2022) - [Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems](https://mlsanity.com/paper/2203.12981/) (2022) ## Built on by (most cited first, 18 of 18) - [Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions](https://mlsanity.com/paper/2602.22680/) (2026) - [Evaluating LLMs in Finance Requires Explicit Bias Consideration](https://mlsanity.com/paper/2602.14233/) (2026) - [Verbalized Confidence Triggers Self-Verification: Emergent Behavior Without Explicit Reasoning Supervision](https://mlsanity.com/paper/2506.03723/) (2025) - [Completion neq Collaboration: Scaling Collaborative Effort with Agents](https://mlsanity.com/paper/2510.25744/) (2025) - [RoleConflictBench: A Benchmark of Role Conflict Scenarios for Evaluating LLMs' Contextual Sensitivity](https://mlsanity.com/paper/2509.25897/) (2025) - [Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation](https://mlsanity.com/paper/2602.16990/) (2026) - [Personalized to Persuade: The Effects of Contextualization and Warmth on Trust and Reliance in Conversational AI](https://mlsanity.com/paper/2605.31275/) (2026) - [Synthesizing Behaviorally-Grounded Reasoning Chains: A Data-Generation Framework for Personal Finance LLMs](https://mlsanity.com/paper/2509.14180/) (2025) - [PREFINE: Personalized Story Generation via Simulated User Critics and User-Specific Rubric Generation](https://mlsanity.com/paper/2510.21721/) (2025) - [One Size Fits None: Heuristic Collapse in LLM Investment Advice](https://mlsanity.com/paper/2604.23837/) (2026) - [Fin-Analyst at FinMMEval 2026 Task 3: A Live Hybrid Trading Agent with LLM Specialists and Rule-Based Signals](https://mlsanity.com/paper/2607.12233/) (2026) - [Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis](https://mlsanity.com/paper/2510.01115/) (2025) - [Personalized Chain-of-Thought Summarization of Financial News for Investor Decision Support](https://mlsanity.com/paper/2511.05508/) (2025) - [FinForge: Semi-Synthetic Financial Benchmark Generation](https://mlsanity.com/paper/2601.06747/) (2026) - [Pro-AI Bias in Large Language Models](https://mlsanity.com/paper/2601.13749/) (2026) - [Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data](https://mlsanity.com/paper/2606.29793/) (2026) - [PersuaRL: Reinforcement Learning-Driven Multi-Expert Selection for Persuasive Dialogue Generation in Insurance](https://mlsanity.com/paper/2609.01188/) (2026) - [Annie, Are You Okay? How Style- and Context-Based Personalization Shape AI-Assisted Decision-Making](https://mlsanity.com/paper/2609.24644/) (2026) ## Abstract (arXiv) Large language model-based agents are becoming increasingly popular as a low-cost mechanism to provide personalized, conversational advice, and have demonstrated impressive capabilities in relatively simple scenarios, such as movie recommendations. But how do these agents perform in complex high-stakes domains, where domain expertise is essential and mistakes carry substantial risk? This paper investigates the effectiveness of LLM-advisors in the finance domain, focusing on three distinct challenges: (1) eliciting user preferences when users themselves may be unsure of their needs, (2) providing personalized guidance for diverse investment preferences, and (3) leveraging advisor personality to build relationships and foster trust. Via a lab-based user study with 64 participants, we show that LLM-advisors often match human advisor performance when eliciting preferences, although they can struggle to resolve conflicting user needs. When providing personalized advice, the LLM was able to positively influence user behavior, but demonstrated clear failure modes. Our results show that accurate preference elicitation is key, otherwise, the LLM-advisor has little impact, or can even direct the investor toward unsuitable assets. More worryingly, users appear insensitive to the quality of advice being given, or worse these can have an inverse relationship. Indeed, users reported a preference for and increased satisfaction as well as emotional trust with LLMs adopting an extroverted persona, even though those agents provided worse advice.