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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
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Fraudulent data might be labelled
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Decision Tree
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Random Forest
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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
Description:
Explore fraud detection techniques using machine learning in this comprehensive webinar. Learn to combat fraud using KNIME, a free low-code tool, without writing code or relying on if-then rules. Discover approaches for both labeled and unlabeled data, including random forest, autoencoders, visualizations, and statistical methods. Master the use of Isolation Forest and DBSCAN algorithms for detecting fraudulent activity. Gain practical skills in implementing various fraud detection techniques using the KNIME Analytics Platform, from basic decision trees to advanced deep learning methods.

Approaches to Fraud Detection - Autoencoder and Isolation Forest - Fraud Detection Using ML

Data Science Dojo
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