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Analysis Study Research Paper | Business and Finance | Volume 15 Issue 5, May 2026 | Pages: 819 - 823 | India
Next-Generation Robo-Advisory Systems: Integrating Dynamic Optimization, Reinforcement Learning, and Explainable AI for Robust Wealth Management
Abstract: The rapid digitalization of the financial advisory industry has catalyzed the widespread adoption of robo-advisors, which provide automated, algorithm-driven wealth management services with minimal human intervention. While these platforms democratize access to financial planning by lowering minimum investment thresholds and reducing management fees, they frequently struggle with robust long-term asset allocation, regulatory explainability, and the cultivation of user trust. This paper proposes a comprehensive, multi-tiered robo-advisory framework that transitions away from static portfolio optimization by integrating Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL) for dynamic, goal-based wealth management. Furthermore, the proposed architecture incorporates explainable clustering techniques and Large Language Model (LLM) agents to enhance transparency, regulatory compliance, and user interaction. By bridging the gap between advanced computational finance and human-centric algorithmic design, this research provides a holistic blueprint for developing reliable, adaptive, and trustworthy autonomous financial advisors.
Keywords: Robo advisory, Wealth management, Deep reinforcement learning, Model predictive control, Explainable finance
How to Cite?: Dr. Surabhi Pachori, "Next-Generation Robo-Advisory Systems: Integrating Dynamic Optimization, Reinforcement Learning, and Explainable AI for Robust Wealth Management", Volume 15 Issue 5, May 2026, International Journal of Science and Research (IJSR), Pages: 819-823, https://www.ijsr.net/getabstract.php?paperid=SR26422123025, DOI: https://dx.dx.doi.org/10.21275/SR26422123025