What is Anomaly Detection?
ML technique identifying unusual patterns, outliers, or deviations from expected behavior, critical for fraud detection, security, and quality control
Summary
- Employs statistical methods (z-score, Grubbs test), clustering algorithms (DBSCAN, isolation forests), and deep learning approaches (autoencoders, GANs) to identify deviations from normal behavior in high-dimensional datasets
- Applications include credit card fraud detection, network intrusion detection, manufacturing defect identification, healthcare diagnosis of rare conditions, and predictive maintenance flagging equipment failures before they occur
- Challenges include high false positive rates in imbalanced datasets, adapting to evolving normal patterns, interpretability of anomaly scores, and determining appropriate sensitivity thresholds balancing detection accuracy with operational overhead
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).
