Introduction to Fraud and Anomaly Detection Online

Apr 13, 2021 · ,

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

Event organizers
Online Event
Online Event

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