Course Outline
Introduction to Machine Learning in Finance
- An overview of AI and ML applications within the financial industry.
- Exploration of machine learning types (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.
- Examining financial datasets using Pandas and NumPy.
- Creating data visualizations with Matplotlib and Seaborn.
Supervised Learning for Financial Prediction
- Techniques involving linear and logistic regression.
- Implementation of decision trees and random forests.
- Assessment of model performance using metrics such as accuracy, precision, recall, and AUC.
Unsupervised Learning and Anomaly Detection
- Application of clustering techniques (e.g., K-means, DBSCAN).
- Use of Principal Component Analysis (PCA).
- Identification of outliers for fraud prevention purposes.
Credit Scoring and Risk Modeling
- Development of credit scoring models using logistic regression and tree-based algorithms.
- Strategies for handling imbalanced datasets in risk-related applications.
- Ensuring model interpretability and fairness in financial decision-making processes.
Fraud Detection with Machine Learning
- Overview of common types of financial fraud.
- Utilizing classification algorithms for anomaly detection.
- Approaches for real-time scoring and deployment.
Model Deployment and Ethics in Financial AI
- Deploying models using Python, Flask, or cloud platforms.
- Addressing ethical considerations and regulatory compliance (e.g., GDPR, explainability).
- Monitoring and retraining models within production environments.
Summary and Next Steps
Requirements
- A solid understanding of basic statistics and financial principles.
- Practical experience with Excel or other data analysis tools.
- Foundational 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
