Anomaly Detection
AI techniques that identify unusual patterns or outliers in data that may indicate problems, opportunities, or fraud.
Definition
Anomaly detection algorithms automatically identify data points, patterns, or behaviors that deviate significantly from established norms. These systems learn normal patterns and flag unusual occurrences that may require investigation or action.
Advanced anomaly detection adapts to changing baselines and distinguishes between benign variations and genuinely concerning outliers, reducing false positives while maintaining sensitivity to important anomalies.
Why It Matters
Early detection of anomalies prevents fraud, identifies system failures before they cause damage, and reveals unexpected opportunities or insights hidden in routine data patterns.
Businesses use anomaly detection to protect assets, maintain system reliability, and discover insights that would be impossible to find through manual analysis of large datasets.
Examples in Practice
Credit card companies detect fraudulent transactions by identifying spending patterns that deviate from individual customer norms and typical merchant behaviors.
Manufacturing systems monitor equipment sensors to detect anomalies that predict machinery failures before they cause production disruptions.
Network security systems identify unusual traffic patterns that may indicate cyber attacks, data breaches, or system compromises.