Next-Generation Wealth Advisory : A Multi-Agent Hybrid AI Model - Víteček, David; Umlauf, Miroslav; Ngenda, Duncan; Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky (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.
Validation in a simulated enterprise environment demonstrated high consistency, explainability, and workflow traceability, confirming the technical feasibility of a “one-client–one-AI-advisor” operating model. The empirical findings show that hybrid architectures outperform traditional advisory processes in scalability, personalization, and error reduction, while remaining compatible with supervisory review and compliance requirements. The thesis concludes that hybrid multi-agent systems represent a credible and strategically advantageous direction for next-generation wealth management. As validation mechanisms mature and regulatory frameworks adapt, advisory workflows are expected to shift progressively toward AI-driven, self-validating models with human oversight reserved for exceptional or high-impact decisions.
Vedoucí práce: Miroslav Umlauf
Závěrečná práce - Institut celoživotního vzdělávání (MBA)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2026
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2026
Způsob přístupu: Internet