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Course Outline
AI Fundamentals for WealthTech
- An overview of the WealthTech innovation landscape
- Core AI technologies: supervised learning, NLP, and recommender systems
- Comparing robo-advisors with hybrid advisory models
Bespoke Financial Recommendations
- Analysing user segmentation and profiling strategies
- Behavioural finance: identifying data sources and modelling user intent
- Developing recommendation engines aligned with financial goals and portfolios
Natural Language and Conversational AI
- Utilising NLP for investor sentiment analysis and client interaction
- Prompt engineering tailored for financial advisory assistants
- Implementing chatbots, voice assistants, and hybrid support platforms
AI-Driven Portfolio Construction
- Risk profiling enhanced by machine learning
- Dynamic portfolio rebalancing powered by AI
- Integrating ESG criteria and custom constraints into AI models
User Experience and Engagement
- Interface design prioritising transparency and trust
- Explainable AI within client-facing tools
- Personal finance dashboards and gamification techniques
Compliance, Ethics, and Regulation
- Regulatory frameworks governing digital advisory services (e.g., MiFID II, SEC)
- Ethics in algorithmic advice: addressing bias, suitability, and fairness
- Ensuring auditability and robust model documentation in WealthTech
Constructing the Intelligent Advisory Stack
- Technology architecture for AI-based wealth platforms
- Deciding between internal development and integration with fintech providers
- Emerging trends: hyper-personalisation, generative interfaces, and LLM integration
Recap and Future Directions
Requirements
- A solid grasp of financial advisory and wealth management principles
- Practical experience with digital financial products or data analytics
- Foundational knowledge of Python or comparable data tools
Target Audience
- Wealth management specialists
- Financial advisors
- Product designers
14 Hours
Testimonials (1)
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