Finance and insurance

1. INTRODUCTION TO THE COURSE • Risk management in banking • Financial risks 2. THE ROLE OF ARTIFICIAL INTELLIGENCE IN BANKING AND FINANCE • Machine Learning, Deep Learning, and Data Science • Applications and examples in Banking and Finance (case studies presentation) 3. INTRODUCTION TO PYTHON AND (FINANCIAL) DATA GATHERING • Describing the main Python packages for finance • Describing the main sources of financial data • Analyzing the different database structures • Exploratory data analysis and Descriptive statistics 4. SUPERVISED LEARNING: OVERVIEW • Regression (specific skills for ML applications) • Support Vector Machine • K-Nearest Neighbors • Linear Discriminant Analysis • Classification and Regression Trees 5. MODEL PERFORMANCE • Overfitting and Underfitting • Cross Validation • Evaluation Metrics 6. SUPERVISED LEARNING: REGRESSION • Linear Regression (specific skills for ML applications) • Logistic Regression (specific skills for ML applications) • Polynomial Regression (specific skills for ML applications) • Applications and examples in Banking and Finance (i.e. determinants of bank default; asset price prediction; ...): coding exercises and academic papers discussion 7. SUPERVISED LEARNING: CLASSIFICATION • Classification Predictive Modeling • Binary Classification • Multi-Label Classification • Imbalanced Classification • Applications and examples in Banking and Finance (i.e. loan defaults; fraud detection; ...): coding exercises and academic papers discussion 8. ARTIFICIAL NEURAL NETWORKS • Architecture • Training • Hyperparameters • Applications and examples in Banking and Finance (i.e. determinants of bank default; ...): coding exercises and academic papers discussion 9. UNSUPERVISED LEARNING: OVERVIEW • Dimensionality reduction • Clustering 10. MODEL PERFORMANCE • Evaluation Metrics 11. UNSUPERVISED LEARNING: DIMENSIONALITY REDUCTION • Feature selection • Feature extraction • Applications and examples in Banking and Finance (i.e. risk management; ...): coding exercises and academic papers discussion 12. UNSUPERVISED LEARNING: CLUSTERING • K-means • Hierarchical clustering • Applications and examples in Banking and Finance (i.e. bank business models identi- fication; ...): coding exercises and academic papers discussion 13. NATURAL LANGUAGE PROCESSING • Topic modeling • Sentiment analysis • Applications and examples in Banking and Finance (i.e. trading strategies; crypto sentiment evaluation; ...): coding exercises and academic papers discussion 14. ETHICS AND TRANSPARENCY IN ARTIFICIAL INTELLIGENCE • (How to deal with) applications and examples 15. PROJECT PRESENTATIONS AND CONCLUSION OF THE COURSE • Group presentations • Wrapping up of the course 16. REAL WORLD EXPERIENCES • Lessons from regulators (European Central Bank; Bank of Italy; ...) • Practitioners (Bankers; Financial or industrial companies; ...)

There are no binding prerequisites. However, students should preferably know the main aspects of bank management (i.e. risk management) and master basic statics for analyzing financial data.

Tatsat, H., Puri, S., & Lookabaugh, B. (2020). Machine Learning and Data Science Blueprints for Finance. O’Reilly Media.

Non mandatory

Grading system • Final Exam – ATTENDING STUDENTS * Group Project (with class presentation) * Individual written examination – NON-ATTENDING STUDENTS * Individual written examination • Weights – ATTENDING STUDENTS * Course participation (attendance and assignments): 5/30 * Group Project evaluation: 15/30 * Individual written examination: 13/30 – NON-ATTENDING STUDENTS * Individual written examination: 33/30

1. INTRODUCTION TO THE COURSE • Risk management in banking • Financial risks 2. THE ROLE OF ARTIFICIAL INTELLIGENCE IN BANKING AND FINANCE • Machine Learning, Deep Learning, and Data Science • Applications and examples in Banking and Finance (case studies presentation) 3. INTRODUCTION TO PYTHON AND (FINANCIAL) DATA GATHERING • Describing the main Python packages for finance • Describing the main sources of financial data • Analyzing the different database structures • Exploratory data analysis and Descriptive statistics 4. SUPERVISED LEARNING: OVERVIEW • Regression (specific skills for ML applications) • Support Vector Machine • K-Nearest Neighbors • Linear Discriminant Analysis • Classification and Regression Trees 5. MODEL PERFORMANCE • Overfitting and Underfitting • Cross Validation • Evaluation Metrics 6. SUPERVISED LEARNING: REGRESSION • Linear Regression (specific skills for ML applications) • Logistic Regression (specific skills for ML applications) • Polynomial Regression (specific skills for ML applications) • Applications and examples in Banking and Finance (i.e. determinants of bank default; asset price prediction; ...): coding exercises and academic papers discussion 7. SUPERVISED LEARNING: CLASSIFICATION • Classification Predictive Modeling • Binary Classification • Multi-Label Classification • Imbalanced Classification • Applications and examples in Banking and Finance (i.e. loan defaults; fraud detection; ...): coding exercises and academic papers discussion 8. ARTIFICIAL NEURAL NETWORKS • Architecture • Training • Hyperparameters • Applications and examples in Banking and Finance (i.e. determinants of bank default; ...): coding exercises and academic papers discussion 9. UNSUPERVISED LEARNING: OVERVIEW • Dimensionality reduction • Clustering 10. MODEL PERFORMANCE • Evaluation Metrics 11. UNSUPERVISED LEARNING: DIMENSIONALITY REDUCTION • Feature selection • Feature extraction • Applications and examples in Banking and Finance (i.e. risk management; ...): coding exercises and academic papers discussion 12. UNSUPERVISED LEARNING: CLUSTERING • K-means • Hierarchical clustering • Applications and examples in Banking and Finance (i.e. bank business models identi- fication; ...): coding exercises and academic papers discussion 13. NATURAL LANGUAGE PROCESSING • Topic modeling • Sentiment analysis • Applications and examples in Banking and Finance (i.e. trading strategies; crypto sentiment evaluation; ...): coding exercises and academic papers discussion 14. ETHICS AND TRANSPARENCY IN ARTIFICIAL INTELLIGENCE • (How to deal with) applications and examples 15. PROJECT PRESENTATIONS AND CONCLUSION OF THE COURSE • Group presentations • Wrapping up of the course 16. REAL WORLD EXPERIENCES • Lessons from regulators (European Central Bank; Bank of Italy; ...) • Practitioners (Bankers; Financial or industrial companies; ...)

There are no binding prerequisites. However, students should preferably know the main aspects of bank management (i.e. risk management) and master basic statics for analyzing financial data.

Tatsat, H., Puri, S., & Lookabaugh, B. (2020). Machine Learning and Data Science Blueprints for Finance. O’Reilly Media.

Non mandatory

Grading system • Final Exam – ATTENDING STUDENTS * Group Project (with class presentation) * Individual written examination – NON-ATTENDING STUDENTS * Individual written examination • Weights – ATTENDING STUDENTS * Course participation (attendance and assignments): 5/30 * Group Project evaluation: 15/30 * Individual written examination: 13/30 – NON-ATTENDING STUDENTS * Individual written examination: 33/30