This white paper provides a comprehensive overview of anomaly detection, explaining why it is critical for identifying unexpected behavior in large and complex datasets. It differentiates anomalies from outliers and explores common use cases including manufacturing defects, equipment monitoring, fraud detection, and healthcare claims analysis. The paper reviews key techniques such as visual discovery, supervised learning, and unsupervised learning, with a deep dive into autoencoder-based machine learning models. Emphasis is placed on combining human expertise with AI to reduce false positives and enable timely, confident intervention.

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