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Fraud and Outlier Detection
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Classroom Contents
Approaches to Fraud Detection - Autoencoder and Isolation Forest - Fraud Detection Using ML
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- 1 Introduction
- 2 KNIME Analytics Platform
- 3 KNIME nodes & workflow
- 4 Goals for the Session
- 5 Fraud is all around us
- 6 Potentially fraudulent data
- 7 Fraudulent data might be labelled
- 8 Decision Tree
- 9 Random Forest
- 10 Advanced: Sampling Strategies
- 11 Finding fraud through deep learning
- 12 A neural autoencoder in KNIME
- 13 Walk through how to do the same task using unlabeled data Jinwei
- 14 Fraud and Outlier Detection
- 15 Finding Outliers: Statistics
- 16 Demo IQR and Z-score Implementation in KNIME
- 17 DBSCAN
- 18 Summary
- 19 Useful Fraud-related links
- 20 Useful KNIME-related links
- 21 Q&A
