LIVE TRAINING: April 20th: Advanced Fraud Modeling
Training duration: 4 hours (Hands-on)
Aric LaBarr, PhD
Aric LaBarr, PhD
Associate Professor of Analytics | Institute for Advanced Analytics at NC State University
Use network analysis to create good features for fraud models like centrality and connectivity
Properly oversample or undersample a rare event data set as well as use synthetic sampling techniques like SMOTE
Build a supervised fraud classification model using one of the following: logistic regression, tree based algorithms, and naive Bayes models
Build a supervised NOT-fraud classification model using one of the above techniques
Interpret a complicated model using LIME
1. Review of Fraud
2. Data Preparation
3. Supervised Fraud Models
4. Clustering and Implementation
Introductory R/Python
Basic introduction to supervised modeling
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