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Course Outline
Introduction to Machine Learning in Finance
- Overview of AI and ML in the financial industry
- Types of machine learning (supervised, unsupervised, reinforcement learning)
- Case studies in fraud detection, credit scoring, and risk modeling
Python and Data Handling Basics
- Using Python for data manipulation and analysis
- Exploring financial datasets with Pandas and NumPy
- Data visualization using Matplotlib and Seaborn
Supervised Learning for Financial Prediction
- Linear and logistic regression
- Decision trees and random forests
- Evaluating model performance (accuracy, precision, recall, AUC)
Unsupervised Learning and Anomaly Detection
- Clustering techniques (K-means, DBSCAN)
- Principal Component Analysis (PCA)
- Outlier detection for fraud prevention
Credit Scoring and Risk Modeling
- Building credit scoring models using logistic regression and tree-based algorithms
- Handling imbalanced datasets in risk applications
- Model interpretability and fairness in financial decision-making
Fraud Detection with Machine Learning
- Common types of financial fraud
- Using classification algorithms for anomaly detection
- Real-time scoring and deployment strategies
Model Deployment and Ethics in Financial AI
- Deploying models with Python, Flask, or cloud platforms
- Ethical considerations and regulatory compliance (e.g., GDPR, explainability)
- Monitoring and retraining models in production environments
Summary and Next Steps
Requirements
- An understanding of basic statistics and financial concepts
- Experience with Excel or other data analysis tools
- Basic programming knowledge (preferably in Python)
Audience
- Financial analysts
- Actuaries
- Risk officers
21 Hours
