Robo-Advisors & Personalized Finance: AI for Automated Wealth Management
artificial-intelligence-ai.
Course Modules:
Module 1: Introduction to Robo-Advisors
What are robo-advisors and how do they work?
Traditional advisory vs. algorithmic advisory
Key components: onboarding, asset allocation, rebalancing
Module 2: User Profiling and Risk Assessment
Collecting user data: income, goals, time horizon, risk tolerance
Building risk scoring models (KYC and behavioral factors)
Clustering and segmentation techniques for user personas
Module 3: Portfolio Recommendation Engines
Modern Portfolio Theory and asset diversification
Optimization algorithms: mean-variance, Black-Litterman
Auto-generation of portfolios by risk class and objective
Module 4: Rebalancing, Goal Tracking & Engagement
Dynamic rebalancing algorithms
Alerts, nudges, and financial wellness recommendations
Personalization with reinforcement learning
Module 5: System Integration, Compliance & UX
APIs for brokerage integration (e.g., Alpaca, Plaid)
Financial regulations (SEC, MiFID II, GDPR)
UI/UX for trust and clarity in digital finance
Module 6: Capstone Project – Build a Robo-Advisory Demo
Design a simple robo-advisor for investment or saving goals
Include onboarding, profiling, allocation, and rebalancing logic
Submit a working prototype, simulation results, and user interface
Tools & Technologies Used:
Python
Pandas, NumPy, Scikit-learn
Streamlit or Flask (for front-end UI)
Plotly, Matplotlib (for portfolio visualization)
Target Audience:
Fintech developers and wealth managers
AI and finance students
Product teams building personal finance tools
Innovators focused on accessible investing platforms
Global Learning Benefits:
Understand the full pipeline of an AI-based financial advisor
Build accessible, scalable, and compliant investment tools
Offer personalized services to underserved or emerging markets
Combine AI, finance, and UX for impactful fintech innovation
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