🎓 Course Modules:
💼 Module 1: Introduction to AI in Finance
Evolution of AI in the financial sector
Key FinTech innovations powered by AI
Regulatory overview (e.g., GDPR, PSD2, Open Banking)
📊 Module 2: Financial Data Analysis & Preprocessing
Types of financial data: time series, transactions, market data
Cleaning and normalizing data for modeling
Handling missing data and outliers in financial contexts
🧠 Module 3: Machine Learning for Risk Management
Credit risk modeling using supervised learning
Loan default prediction and scorecard models
Applications in insurance and underwriting
🛡️ Module 4: Fraud Detection with AI
Anomaly detection techniques (Isolation Forest, Autoencoders)
Real-time fraud monitoring systems
Behavioral pattern recognition in transactions
💹 Module 5: Algorithmic Trading & Market Forecasting
Basics of quantitative trading strategies
Predicting stock prices with LSTM and XGBoost
Backtesting trading algorithms and portfolio simulation
💳 Module 6: Credit Scoring & Loan Automation
Traditional vs AI-based credit scoring
Using NLP for processing loan applications
Building credit risk models with fairness considerations
🤖 Module 7: Robo-Advisors & Personalized Finance
How robo-advisors work (rules-based and ML-driven)
Portfolio optimization with AI
Personal financial planning tools
🗣️ Module 8: NLP in Financial Services
Extracting insights from financial news and reports
Sentiment analysis on earnings calls and stock mentions
Chatbots and virtual agents in banking
⚖️ Module 9: Ethics, Compliance & Explainability in FinTech
Regulatory compliance and explainable AI
Fair lending, algorithmic bias, and transparency
AI governance in financial institutions
✅ Module 10: Capstone Project
Choose a project such as:
Building a credit scoring model
Developing a stock price prediction tool
Designing a fraud detection dashboard
Project presentation and peer evaluation
🧠Master Study NLP Fundamentals: The Foundation of Language Understanding in AI
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