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Course Outline

Introduction to Machine Learning in Finance

  • Overview of AI and ML applications in the financial industry
  • Types of machine learning (supervised, unsupervised, reinforcement learning)
  • Case studies covering fraud detection, credit scoring, and risk modeling

Python and Data Handling Fundamentals

  • Leveraging Python for data manipulation and analysis
  • Investigating financial datasets using Pandas and NumPy
  • Visualising data with Matplotlib and Seaborn

Supervised Learning for Financial Forecasting

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

Unsupervised Learning and Anomaly Detection

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

Credit Scoring and Risk Modeling

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

Fraud Detection Using Machine Learning

  • Identifying common types 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 via Python, Flask, or cloud platforms
  • Addressing ethical considerations and regulatory compliance (e.g., GDPR, explainability)
  • Monitoring and retraining models within production environments

Summary and Next Steps

Requirements

  • Foundational understanding of statistics and financial principles
  • Proficiency with Excel or similar data analysis tools
  • Basic programming knowledge, ideally in Python

Target Audience

  • Financial analysts
  • Actuaries
  • Risk officers
 21 Hours

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