Case Study

Anomaly Detection: Detecting Unknown Patterns in Anti-Money

Anomaly Detection: Detecting Unknown Patterns in Anti-Money

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KNIME AG helped Rabobank scale their money laundering detection efforts by developing self-service anomaly detection workflows used by over 300 auditors globally. KNIME enabled the translation of senior auditors’ insights into business rules applied across entire portfolios, improving quality assurance by testing full populations rather than samples. The platform’s advanced capabilities in anomaly detection, cluster analysis, and predictive AI helped identify unknown suspicious patterns, empowering auditors without coding skills to independently conduct data-driven analyses, boosting audit speed, consistency, and risk coverage.

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