Research on Personalized Asset Allocation Using AI Agents in Robo-Advisory Scenarios

Abstract

This review paper provides a systematic review of personalized asset allocation facilitated by AI agents within robo-advisory platforms. Robo-advisors, employing algorithms to automate investment decisions, are increasingly incorporating sophisticated AI techniques to tailor portfolios to individual investor needs and preferences. This paper investigates the evolution of these AI-driven systems, examining key themes such as risk profiling, dynamic asset allocation strategies, and the integration of behavioral finance principles. A comparative analysis of current methodologies highlights their strengths and limitations, particularly concerning transparency, explainability, and robustness in volatile market conditions. Furthermore, the review addresses the challenges associated with data privacy, regulatory compliance, and the potential for algorithmic bias. By synthesizing current research, we identify promising future directions, including the development of more interpretable AI models, the incorporation of alternative data sources, and the creation of more seamless and personalized user experiences. This review aims to provide a comprehensive overview of the current landscape, fostering a deeper understanding of the opportunities and challenges presented by AI-powered personalized asset allocation in robo-advisory contexts.