| Date | Day | Time | Duration |
| 21 Nov | Sunday | 0900-17:00 GST/GMT+4 | 8 Hours |
| 22 Nov | Monday | 0900-17:00 GST/GMT+4 | 8 Hours |
| 23 Nov | Tuesday | 0900-17:00 GST/GMT+4 | 8 Hours |
It is estimated that a typical organization loses about 5 percent of its revenues due to fraud each year. In this course, you will learn how machine learning can be used to fight frauds: you will understand when and how to apply supervised learning algorithms to detect fraudulent behavior similar to past ones, as well as unsupervised learning methods to discover novel types of fraudulent activities.
The course will not focus on the mathematics or theory, but on the practical applications: the course will provide a mix of technical and theoretical insights and shows you how to practically implement fraud detection models.
Moreover, during the course you will understand how to deal with the typical challenges of the fraud analytics task (e.g., data scarcity and imbalancing) and will get advice from real-life experience to help you prevent making common mistakes in the fraud detection domain.
- Frauds: Definition and Types - Fraud Detection and Prevention - Big data and Analytics - Fraud analytics process model - Develop a data-driven fraud-detection system - Data Preprocessing Step
- Graphical and Statistical Outlier Detection - Clustering - Evaluating and Interpreting Clustering Solutions
- Linear Regression - Logistic Regression - Decision Trees - Neural Networks - Support Vector Machines - Ensemble Methods: Random Forests
- Splitting Up the Data Set - Performance Metrics
- Demo and hands on a real fraud analytical engine
Michele received his Ph.D. degree cum laude in Information Technology from Politecnico di Milano in Italy, where he is currently a Contract Professor and a Postdoctoral Researcher working as part of the System Security group inside the Dipartimento di Elettronica, Informazione e Bioingegneria.
His research revolves around the application of machine learning methods to various cybersecurity-related fields, ranging from cyber-physical and automotive systems to binary analysis, going through fraud and intrusion detection. In particular, his research focuses mainly on the financial fraud detection task, where he worked on the analysis of advanced financial threats such as banking Trojans, on the design of frameworks to identify and timely detect fraudulent transactions, and on the security evaluation of detection systems against adversarial machine learning attacks.
He is actively involved in research projects funded by the European Union, and he is also co-founder of Banksealer, a Fintech spinoff of Politecnico di Milano.
