Wealth Management |

AI Application in FinTech

Fri, 17 Jul 2026 00:00:00 +0000

课程简介

本课程面向具有基础数理背景的学生，系统介绍人工智能在金融科技领域的技术原理与实际应用。课程分为两大部分：第一部分从图灵测试出发，梳理 AI 技术的演进脉络，涵盖监督学习、无监督学习、强化学习三大范式，深入讲解神经网络、注意力机制、Transformer 架构、大语言模型（LLM）以及检索增强生成（RAG）和 AI Agent 等前沿概念。第二部分聚焦金融科技应用场景，包括智能投顾（Robo-Advisory）、组合优化与现代投资组合理论、直接指数化（Direct Indexing）、税收亏损收割（Tax-Loss Harvesting），以及如何将多角色 AI Agent 系统应用于投资研究与决策流程。课程通过真实案例和实操演示，帮助学生理解如何将机器学习与大模型能力转化为可部署的金融产品与服务。

资料下载

A Two-Layer Attribution Framework for Portfolios Built from Mixed-Asset Products

Thu, 01 Jan 2026 00:00:00 +0000

AI-Driven Automatic Wealth Management Solution

Sun, 31 Mar 2024 00:00:00 +0000

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.

AI-Driven MPF Retirement Advisory System

Mon, 01 Jan 2024 00:00:00 +0000

Problem

Hong Kong’s Mandatory Provident Fund (MPF) system requires participants to actively select from a range of constituent funds within their employer-sponsored scheme, yet most participants lack the financial knowledge or time to make well-informed, dynamic fund-selection decisions. Retirement adequacy outcomes are systematically poor when fund allocation is not regularly reviewed.

Method

AQUMON developed an AI-driven MPF investment advisory system combining algorithmic goal-based personalisation, lifecycle and asset-liability management (ALM) informed optimisation, and explainable recommendation workflows. The system integrates personalised retirement-gap analytics, risk profiling, and constraint-aware fund selection within a compliance-ready architecture suitable for deployment in regulated insurance and pension distribution environments. Distributed computation enables real-time recommendation generation across large participant bases.

Deployment

The system has been deployed with multiple MPF trustees and insurance providers in Hong Kong, including AIA (integrated into the AIA+ mobile app) and BCT Group (MARIO, BCT’s market-first AI advisor). The platform is designed to support approximately seven million Hong Kong MPF participants, with cumulative assets under advice exceeding USD 10 billion.

Research connection

This project draws on doctoral and post-doctoral research in stochastic control and optimal withdrawal policy design. The production deployment extends those theoretical results into a regulated, real-world context.