Anomaly Detection & Introduction to Fraud Modelling is a workshop dedicated to fraud and anomaly detection.
Anomaly Detection & Introduction to Fraud Modelling covers topics such as:
- Anomalies using statistical techniques like z-scores, robust z-scores, Mahalanobis distances, k-nearest neighbors (k-NN), and local outlier factor (LOF)
- Good features (recency, frequency, and monetary value as well as categorical transformations) for detecting and preventing fraud
- Anomalies using machines learning approaches like isolation forests and classifier adjusted density estimation (CADE)
Anomaly Detection & Introduction to Fraud Modelling brings together:
- Engineers
- Data Scientists
- Software Engineers
- Analysts
