Approaches to Fraud and Anomaly Detection: Autoencoder and Isolation Forest

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 detection such as the random forest. Then we will cover a deep learning technique, the autoencoder, to find fraudulent data points.

In the second part of the webinar, we will focus on data without labels of fraudulent activity using visualizations, classical statistics, and machine learning. You will learn how easy it is to generate multiple visualizations, perform statistical analysis, and use two machine learning algorithms - Isolation Forest and DBSCAN - all to detect fraudulent activity in the free, open-source KNIME Analytics Platform.

In this session you will learn:
- How to identify fraud using a variety of techniques including visualizations, statistics, and machine learning
- How to use machine learning and deep learning algorithms for fraud detection regardless of whether you have labelled data or not

Table of Contents:
00:00 Introduction
01:29 KNIME Analytics Platform
02:51 KNIME nodes & workflow
04:24 Goals for the Session
05:29 Fraud is all around us
07:07 Potentially fraudulent data
07:38 Fraudulent data might be labelled
09:14 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 in KNIME
34:59 DBSCAN
38:20 Summary
38:46 Useful Fraud-related links
39:01 Useful KNIME-related links
40:06 Q&A

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