Introduction to Fraud and Anomaly Detection Online

By the end of the course, participants will be able to:

Develop good features (recency, frequency, and monetary value as well as categorical transformations) for detecting and preventing fraud

Identify anomalies using statistical techniques like z-scores, robust z-scores, Mahalanobis distances, k-nearest neighbors (k-NN), and local outlier factor (LOF)

Identify anomalies using machines learning approaches like isolation forests and classifier adjusted density estimation (CADE)

Visualize these anomalies identified by the above approaches