Anomaly Detection

Anomaly detection works by establishing patterns of normal behavior within a dataset and identifying observations that fall outside those patterns. Depending on the application, anomalies may represent errors, unusual activity, security threats, or unexpected system behavior. Detection methods can use statistical techniques, machine learning algorithms, or deep learning models to identify deviations across large and complex datasets.

Anomalies can be early indicators of fraud, cyberattacks, equipment failures, or data quality problems. Detecting them quickly helps organizations investigate unusual activity, reduce potential damage, and maintain reliable systems. Automated anomaly detection is especially valuable when monitoring large volumes of data where manual analysis is impractical.