AMLD 2021 Workshop – Fraud detection with unsupervised ML
Event details
| Date | 29.09.2021 |
| Hour | 09:00 › 13:00 |
| Location |
STCC
|
| Category | Conferences - Seminars |
| Event Language | English |
⚠️ A valid COVID certificate must be presented on site to enter the event. ⚠️
In many fraud- (or general outlier-) detection situations, labelled data is not available. We therefore need to resort to unsupervised methods to identify points that are somehow untypical. In this workshop, a short introduction will be given that discusses the main outlier detection methods (from the classic LOF to modern algorithms such as Isolation Forest, Autoencoders and Adversarial networks) and appropriate metrics for highly imbalanced datasets.
Then, participants will be given unlabelled datasets to make predictions on. Scores will be compared on a leader board, with the emphasis on comparing techniques.
In many fraud- (or general outlier-) detection situations, labelled data is not available. We therefore need to resort to unsupervised methods to identify points that are somehow untypical. In this workshop, a short introduction will be given that discusses the main outlier detection methods (from the classic LOF to modern algorithms such as Isolation Forest, Autoencoders and Adversarial networks) and appropriate metrics for highly imbalanced datasets.
Then, participants will be given unlabelled datasets to make predictions on. Scores will be compared on a leader board, with the emphasis on comparing techniques.
Links
Practical information
- Informed public
- Registration required