Are Generative AI Agents Effective Personalized Financial Advisors?
Journal:
arXiv
Published Date:
Apr 8, 2025
Abstract
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.
