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Course Outline

Introduction to Machine Learning in Finance

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

Python and Data Handling Essentials

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

Supervised Learning for Financial Prediction

  • Linear and logistic regression
  • Decision trees and random forests
  • Assessing 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 Modelling

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

Fraud Detection with Machine Learning

  • Common types of financial fraud
  • Employing classification algorithms for anomaly detection
  • Real-time scoring and deployment strategies

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 Next Steps

Requirements

  • A solid grasp of basic statistics and financial principles
  • Familiarity with Excel or other data analysis tools
  • Fundamental programming knowledge, ideally in Python

Target Audience

  • Financial analysts
  • Actuaries
  • Risk officers
 21 Hours

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