Development of advanced algorithms for automated investment advisory services in private banking

Posted: Oct 28, 2025

• Dr. Scarlett Dubois
• Dr. Victoria Mendes
• Dr. Zoe Singh

Abstract

The landscape of private banking has undergone significant transformation with the emergence of automated investment advisory services, commonly referred to as robo-advisors. These digital platforms have democratized access to investment management, yet they face substantial limitations when applied to the complex needs of high-net-worth individuals in private banking contexts. Traditional robo-advisory systems primarily employ Modern Portfolio Theory and mean-variance optimization frameworks, which operate under assumptions of normal return distributions and linear relationships that frequently break down during market stress conditions. Furthermore, these systems often fail to incorporate the sophisticated, multi-dimensional preferences that characterize private banking clients, including ethical investment considerations, intergenerational wealth transfer objectives, and complex tax optimization requirements. This research addresses these limitations through the development of a novel algorithmic framework that integrates quantum-inspired computing principles with deep reinforcement learning and advanced natural language processing. Our approach represents a fundamental departure from conventional methodologies by incorporating bio-inspired optimization techniques derived from fungal network growth patterns, which enable more robust portfolio construction across traditional and alternative asset classes. The system's capacity to process both quantitative financial data and qualitative client communications through unified neural architectures establishes a new paradigm for personalized wealth management.