Next-Generation Wealth Advisory : A Multi-Agent Hybrid AI Model - Víteček, David (2025)
The accelerating emergence of AI-native advisory platforms is reshaping the wealth-management industry, creating both an opportunity and a strategic necessity for institutions facing rising client expectations and a shrinking advisor workforce. By 2034, the U.S. market is projected to experience an advisor shortfall of nearly 30%, while investors increasingly demand real-time, hyperpersonalized, and digitally delivered guidance. This thesis responds to these pressures by designing, implementing, and validating a Hybrid AI Advisor—a multi-agent, modular system that enhances traditional advisory workflows with scalable AI capabilities while preserving transparency, auditability, and regulatory alignment. The system integrates several coordinated components: a GPT-based conversational agent that converts unstructured dialogue into structured risk profiles and financial objectives; a rule-based AI Portfolio Selector that maps these profiles to suitable investment strategies with deterministic, auditable logic; and a market-signal engine incorporating indicators such as Squeeze Momentum and Parabolic SAR to contextualize portfolio decisions. These inputs feed into a Python-based rebalancing engine that produces reproducible allocation updates and transaction-ready instructions. Quality and coherence are ensured through a dual validation layer combining cosine-similarity benchmarking and peer-LLM review. All modules operate within a multi-agent orchestration framework enabling transparent data flow, modular extensibility, and human-in-the-loop oversight. A fully functional prototype—implemented using GPT-based agents, MANUS orchestration, Python analytics, and a web-based client interface—was developed and published on GitHub as part of this research.