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Course Outline
AI Foundations for WealthTech
- Overview of WealthTech innovation landscape
- Core AI technologies: supervised learning, NLP, recommender systems
- Robo-advisors vs hybrid advisory models
Personalized Financial Recommendations
- Understanding user segmentation and profiling
- Behavioral finance: data sources and user intent modeling
- Recommendation engines for financial goals and portfolios
Natural Language and Conversational AI
- NLP for investor sentiment and client interactions
- Prompt engineering for financial advisory assistants
- Chatbots, voice assistants, and hybrid support platforms
AI-Enhanced Portfolio Design
- Risk profiling using machine learning
- Dynamic portfolio rebalancing with AI
- Incorporating ESG and custom constraints into AI models
User Experience and Engagement
- Interface design for transparency and trust
- Explainable AI in client-facing tools
- Personal finance dashboards and gamification
Compliance, Ethics, and Regulation
- Regulatory frameworks for digital advisory services (e.g. MiFID II, SEC)
- Ethics in algorithmic advice: bias, suitability, and fairness
- Auditability and model documentation in WealthTech
Building the Intelligent Advisory Stack
- Technology architecture for AI-based wealth platforms
- Internal development vs integration with fintech providers
- Future trends: hyperpersonalization, generative interfaces, LLM integration
Summary and Next Steps
Requirements
- An understanding of financial advisory and wealth management concepts
- Experience with digital financial products or data analysis
- Basic familiarity with Python or related data tools
Audience
- Wealth management professionals
- Financial advisors
- Product designers
14 Hours
