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Approaches to Fraud Detection: Autoencoder, Isolation Forest and More


“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 detection without a single line of code nor brittle if-then rules!

In the first part of this webinar, we will work with labeled 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 labeled data or not
Victor Palacios
Victor Palacios

Data Scientist at KNIME

Victor received his master’s degree in data science from the University of San Francisco and is currently working in the same field at KNIME. He teaches courses on data science, machine learning, and KNIME. His initial projects and interests were in the deception detection space and also in health care, but originally his love of data science blossomed from natural language processing in the translation field.
Jinwei Sun
Jinwei Sun

Data Scientist at KNIME

Jinwei Sun is a data science student at the University of San Francisco and working as a Data Scientist Intern at KNIME. He holds a bachelor’s degree in Business administration with a concentration in information systems, which drove his interests in Data Science and Technology. After a few years of experience in data analytics, Jinwei continues pursuing his career in data science and applying his studies to solve business problems.

We are looking for passionate people willing to cultivate and inspire the next generation of leaders in tech, business, and data science. If you are one of them get in touch with us!