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Course Outline

Introduction to Machine Learning in Finance

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

Python and Data Handling Essentials

  • Leveraging Python for data manipulation and analysis
  • Analyzing financial datasets using Pandas and NumPy
  • Data visualization with Matplotlib and Seaborn

Supervised Learning for Financial Forecasting

  • Linear and logistic regression techniques
  • Decision trees and random forests
  • Assessing model performance (accuracy, precision, recall, 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 via logistic regression and tree-based algorithms
  • Managing imbalanced datasets in risk contexts
  • Ensuring model interpretability and fairness in financial decision-making

Fraud Detection Using Machine Learning

  • Identification of common financial fraud types
  • Utilizing classification algorithms for anomaly detection
  • Real-time scoring and deployment strategies

Model Deployment and Ethical AI in Finance

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

Conclusion and Future Steps

Requirements

  • Familiarity with fundamental statistics and financial principles
  • Proficiency in Excel or similar data analysis tools
  • Foundational programming skills, ideally in Python

Target Audience

  • Financial analysts
  • Actuaries
  • Risk officers
 21 Hours

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