LIVE TRAINING: April 13th : Introduction to Fraud and Anomaly Detection
Training duration: 4 hours (Hands-on)
Aric LaBarr, PhD
Aric LaBarr, PhD
Associate Professor of Analytics | Institute for Advanced Analytics at NC State University
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
1. Introduction to Fraud
2. Data Preparation
3. Anomaly Models
Introductory R/Python
Basic introduction to decision trees (this isn't required, but helpful for understanding)
Basic introduction to classification models like logistic regression, decision trees, etc. (this isn't required, but helpful for understanding)
Access to live training and QA session with the Instructor
Access to the on-demand recording
Certificate of completion