This research evaluates how effectively LLM-based agents can serve as personalized financial advisors in complex, high-risk scenarios where domain expertise is essential.

Key Findings:

• LLM agents demonstrate capabilities in simple recommendation tasks but face significant challenges in financial advising
• Financial advice requires specialized knowledge and risk assessment that current AI systems struggle to provide consistently
• The high-stakes nature of financial decisions amplifies the security and trust concerns when using AI advisors
• Security implications include potential financial losses, privacy concerns, and misplaced user trust

This research highlights critical security considerations for deploying AI systems in domains where errors could lead to significant financial harm, emphasizing the need for appropriate guardrails and human oversight in sensitive financial applications.

Are Generative AI Agents Effective Personalized Financial Advisors?