Fraud & Anomaly Detection via Autoencoder & Isolation Forest | Machine Learning | Community Webinar
An overview of fraud and anomaly detection via Autoencoder and Isolation Forest.
“Experts estimate federal government losses from potential fraud at nearly $150 billion” and new data shows the Federal Trade Commission received 2.8 million reports of fraud in 2021 from consumers. In this webinar, we liked to show how to fight fraud with KNIME, a free and low-code tool, that can perform fraud and anomaly detection without a single line of code nor brittle if-then rules!
In the first part of this webinar, we will work with labelled data to perform classical machine-learning approaches to fraud…
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Chapters (16)
Introduction
1:29
KNIME Analytics Platform
2:51
KNIME nodes & workflow
4:24
Goals for the Session
5:29
Fraud is all around us
7:07
Potentially fraudulent data
7:38
Fraudulent data might be labelled
9:14
Decision Tree
11:53
Random Forest
16:05
Advanced: Sampling Strategies
17:58
Finding fraud through deep learning
19:59
A neural autoencoder in KNIME
20:55
Walk through how to do the same task using unlabeled data (Jinwei)
21:27
Fraud and Outlier Detection
25:01
Finding Outliers: Statistics
30:43
Demo IQR and Z-score Implementation i
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