Course Outline
Introduction to Machine Learning in Finance
- Overview of AI and ML applications in the financial industry.
- Types of machine learning: supervised, unsupervised, and reinforcement learning.
- Case studies covering fraud detection, credit scoring, and risk modeling.
Python and Data Handling Basics
- Leveraging Python for data manipulation and analysis.
- Exploring financial datasets using Pandas and NumPy.
- Data visualization techniques using Matplotlib and Seaborn.
Supervised Learning for Financial Prediction
- Linear and logistic regression.
- Decision trees and random forests.
- Evaluating model performance metrics (accuracy, precision, recall, AUC).
Unsupervised Learning and Anomaly Detection
- Clustering techniques such as K-means and DBSCAN.
- Principal Component Analysis (PCA).
- Outlier detection methods for fraud prevention.
Credit Scoring and Risk Modeling
- Developing credit scoring models using logistic regression and tree-based algorithms.
- Managing imbalanced datasets in risk-related applications.
- Ensuring model interpretability and fairness in financial decision-making.
Fraud Detection with Machine Learning
- Common types of financial fraud.
- Applying classification algorithms for anomaly detection.
- Strategies for real-time scoring and model deployment.
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 within production environments.
Summary and Next Steps
Requirements
- A foundational understanding of basic statistics and financial concepts.
- Experience using Excel or other data analysis tools.
- Basic programming knowledge, preferably 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
