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Course Outline

Introduction to Machine Learning in Finance

  • Overview of AI and ML applications within the financial industry
  • Categories of machine learning (supervised, unsupervised, reinforcement learning)
  • Case studies focusing on fraud detection, credit scoring, and risk modeling

Python and Data Handling Fundamentals

  • Utilizing Python for data manipulation and analysis
  • Exploring financial datasets using Pandas and NumPy
  • Data visualization techniques with Matplotlib and Seaborn

Supervised Learning for Financial Prediction

  • Linear and logistic regression models
  • Decision trees and random forests
  • Assessing model performance through accuracy, precision, recall, and AUC

Unsupervised Learning and Anomaly Detection

  • Clustering methods (K-means, DBSCAN)
  • Principal Component Analysis (PCA)
  • Detecting outliers for fraud prevention

Credit Scoring and Risk Modeling

  • Developing credit scoring models using logistic regression and tree-based algorithms
  • Managing imbalanced datasets in risk-related applications
  • Ensuring model interpretability and fairness in financial decision-making

Fraud Detection with Machine Learning

  • Common forms of financial fraud
  • Applying classification algorithms for anomaly detection
  • Strategies for real-time scoring and deployment

Model Deployment and Ethics in Financial AI

  • Deploying models using Python, Flask, or cloud platforms
  • Ethical considerations and regulatory compliance (e.g., GDPR, explainability)
  • Monitoring and retraining models in production environments

Summary and Future Directions

Requirements

  • Foundational knowledge of basic statistics and financial principles
  • Proficiency with Excel or other data analysis tools
  • Basic programming skills (Python preferred)

Target Audience

  • Financial analysts
  • Actuaries
  • Risk officers
 21 Hours

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