Course Outline
Introduction to Machine Learning in Finance for Government
- Overview of Artificial Intelligence (AI) and Machine Learning (ML) in the Financial Industry
- Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Case Studies in Fraud Detection, Credit Scoring, and Risk Modeling for Government
Python and Data Handling Basics for Government
- Using Python for Data Manipulation and Analysis in Financial Applications
- Exploring Financial Datasets with Pandas and NumPy for Government
- Data Visualization Using Matplotlib and Seaborn for Government
Supervised Learning for Financial Prediction for Government
- Linear and Logistic Regression in Financial Models
- Decision Trees and Random Forests for Predictive Analysis
- Evaluating Model Performance: Accuracy, Precision, Recall, and AUC
Unsupervised Learning and Anomaly Detection for Government
- Clustering Techniques: K-means and DBSCAN for Financial Data
- Principal Component Analysis (PCA) for Dimensionality Reduction
- Outlier Detection for Fraud Prevention in Government Operations
Credit Scoring and Risk Modeling for Government
- Building Credit Scoring Models Using Logistic Regression and Tree-based Algorithms
- Handling Imbalanced Datasets in Risk Applications for Government
- Model Interpretability and Fairness in Financial Decision-making for Government
Fraud Detection with Machine Learning for Government
- Common Types of Financial Fraud in Government Operations
- Using Classification Algorithms for Anomaly Detection in Government
- Real-time Scoring and Deployment Strategies for Government Systems
Model Deployment and Ethics in Financial AI for Government
- Deploying Models with Python, Flask, or Cloud Platforms for Government
- Ethical Considerations and Regulatory Compliance (e.g., GDPR, Explainability) for Government
- Monitoring and Retraining Models in Production Environments for Government
Summary and Next Steps for Government
Requirements
- A solid grasp of fundamental statistical and financial principles
- Proficiency with Excel or other data analysis software
- Basic programming skills, preferably in Python
Audience for Government
- 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
