AI-Driven Automatic Wealth Management Solution

Period: April 2021 – March 2024
Funding: Innovation and Technology Fund (ITF), Hong Kong, Reference No. B/E003/20
Industry partner: Magnum Research Limited (AQUMON)

Problem

End-to-end digital wealth advisory requires integrating client profiling, goal-based portfolio construction, ongoing rebalancing, and monitoring within a scalable, auditable, and compliance-aware system. Existing approaches either rely on static rule-based logic or lack the modularity required for institutional deployment across diverse client segments and regulatory jurisdictions.

Method

The project developed a full-stack AI advisory architecture covering: (i) client profiling and suitability assessment using ML classifiers; (ii) goal-based portfolio construction integrating intertemporal allocation theory with modern estimation techniques; (iii) a hybrid-cloud data foundation supporting global asset universes and real-time feeds; (iv) high-fidelity backtesting with controls against overfitting; and (v) integration with execution infrastructure. Research components addressed model selection, uncertainty quantification in portfolio optimisation, and the design of modular research-validation-deployment pipelines.

Outcome

The project delivered a production-grade automated wealth management architecture deployed across multiple institutional client implementations. It established a reusable technical foundation for AQUMON’s robo-advisory and AI advisory services.