This workflow is the first step of anomaly and fraud detection, which should be performed in Auditing, Accounting & Finance, Risk & Compliance, and Diagnostic Analytics for every data analyst and data scientist.
Furthermore, outlier detection and handling are important parts of the data cleaning stage for ensuring data quality in analytics projects.
This workflow demonstrates how to:
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Detect numeric outliers effectively
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Handle heterogeneous datasets using subgroup analysis
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Automate email alerts with attachment reporting
With just a few steps in KNIME, this workflow provides a simple but powerful approach to anomaly and fraud detection automation.
The workflow is designed for sharing and learning purposes, while also reflecting real-world implementation practices.