Course Outline
Introduction to Machine Learning in Finance
- The role of AI and ML within the financial industry.
- Classification of machine learning approaches (supervised, unsupervised, reinforcement learning).
- Real-world case studies in fraud detection, credit scoring, and risk modeling.
Python Fundamentals and Data Management
- Leveraging Python for data manipulation and analysis.
- Investigating financial datasets using Pandas and NumPy.
- Visualizing data with Matplotlib and Seaborn.
Supervised Learning for Financial Forecasts
- Linear and logistic regression techniques.
- Decision trees and random forests.
- Assessing model efficacy (accuracy, precision, recall, AUC).
Unsupervised Learning and Anomaly Identification
- Clustering methodologies (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 contexts.
- Ensuring model interpretability and fairness in financial decisions.
Fraud Detection via Machine Learning
- Identifying prevalent 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 using Python, Flask, or cloud services.
- Navigating ethical considerations and regulatory compliance (e.g., GDPR, explainability).
- Monitoring and retraining models in production settings.
Conclusion and Future Directions
Requirements
- A solid grasp of basic statistics and financial principles.
- Familiarity with Excel or comparable data analysis tools.
- Foundational programming skills, ideally in Python.
Target Audience
- Financial analysts.
- Actuaries.
- Risk officers.
Testimonials (5)
Possible applications /exercises
Estelle De la Fouchardiere - Advanced Bionics AG
Course - Machine Learning & AI for Finance Professionals
I really enjoyed seeing how using this tool can really improve and automate work. I also appreciated the initial part where we were helped to eliminate our prejudice against artificial intelligence. The examples are wonderful.
chiara di egidio - Advanced Bionics AG
Course - Machine Learning & AI for Finance Professionals
I liked to get knowledge about new possibilities
Maciej Karolczak - Advanced Bionics AG
Course - Machine Learning & AI for Finance Professionals
I like the examples, so we have an idea of what is possible
Deborah Highes
Course - Machine Learning & AI for Finance Professionals
it has opened my mind to new tool that can help me in creating automation
