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Course Outline

AI Foundations for WealthTech

  • Overview of the WealthTech innovation landscape.
  • Core AI technologies: supervised learning, NLP, recommender systems.
  • Robo-advisors compared to hybrid advisory models.

Personalized Financial Recommendations

  • Understanding user segmentation and profiling.
  • Behavioral finance: data sources and modeling user intent.
  • Recommendation engines for financial goals and portfolios.

Natural Language and Conversational AI

  • Using 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 criteria and custom constraints into AI models.

User Experience and Engagement

  • Interface design focused on transparency and trust.
  • Explainable AI in client-facing tools.
  • Personal finance dashboards and gamification strategies.

Compliance, Ethics, and Regulation

  • Regulatory frameworks for digital advisory services (e.g., MiFID II, SEC).
  • Ethical considerations 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 versus integration with fintech providers.
  • Future trends: hyperpersonalization, generative interfaces, and LLM integration.

Summary and Next Steps

Requirements

  • A foundational understanding of financial advisory and wealth management concepts.
  • Experience with digital financial products or data analysis.
  • Basic familiarity with Python or similar data tools.

Audience

  • Wealth management professionals.
  • Financial advisors.
  • Product designers.
 14 Hours

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