Master Study AI

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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