What is Anomaly Detection?
ML technique identifying unusual patterns, outliers, or deviations from expected behavior, critical for fraud detection, security, and quality control
- Anomaly detection is the process of finding data points, events or patterns that deviate from what is considered normal.
- Organizations use anomaly detection to flag issues such as fraud, equipment failures, security breaches or data quality problems early.
- Approaches range from simple rules and thresholds to advanced statistical and machine learning models applied to both historical and streaming data.
Anomaly Detection is the technique of identifying rare events or observations which can raise suspicions by being statistically different from the rest of the observations. Such “anomalous” behavior typically translates to some kind of a problem like credit card fraud, a failing machine, or a cyber attack. In finance, with thousands or millions of transactions to watch, anomaly detection can help point out where an error is occurring, enhancing root cause analysis and quickly getting support on the issue. Anomaly detection helps the monitoring cause of chaos engineering by detecting outliers and informing the responsible parties to act. Machine Learning and AI are increasingly being used for anomaly detection for fraud detection and Anti-Money Laundering (AML).
Additional Resources
The agentic AI playbook for the enterprise
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