Get in Touch

Course Outline

Introduction to Machine Learning in the Financial Sector

  • Overview of AI and ML applications in finance
  • Varieties of machine learning (supervised, unsupervised, reinforcement learning)
  • Case studies focusing on fraud detection, credit scoring, and risk modeling

Python and Fundamentals of Data Management

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

Supervised Learning for Financial Forecasting

  • Linear and logistic regression methods
  • Decision trees and random forest algorithms
  • Assessing model performance metrics (accuracy, precision, recall, AUC)

Unsupervised Learning and Anomaly Identification

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

Credit Scoring and Risk Modeling

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

Fraud Detection via Machine Learning

  • Common forms of financial fraud
  • Applying 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

Recap and Future Steps

Requirements

  • Familiarity with foundational statistics and financial principles
  • Proficiency with Excel or similar data analysis tools
  • Basic programming proficiency, ideally in Python

Target Audience

  • Financial analysts
  • Actuaries
  • Risk managers
 21 Hours

Number of participants


Price per participant

Testimonials (5)

Upcoming Courses

Related Categories