Anomaly Detection

What is Anomaly Detection?

In the realm of artificial intelligence, Anomaly Detection plays a critical role in identifying unusual patterns that do not conform to expected behavior. This technique is widely utilized in various industries, from finance to healthcare, to detect deviations that could signify errors, fraud, or potential threats. Machine learning algorithms are often employed to analyze historical data and establish a baseline of normal behavior. Once the baseline is established, the system can flag anomalies that deviate from this norm. These deviations can then be further investigated to determine if they signify a problem that needs addressing. Anomaly Detection helps businesses proactively manage risks by identifying issues before they escalate into significant problems. It is an essential tool for maintaining the integrity and security of systems and processes.

Anomaly Detection refers to the identification of rare items, events, or observations which raise suspicions by differing significantly from the majority of the data.

Examples

• Credit Card Fraud Detection: Banks use anomaly detection to monitor transactions in real-time. If an unusual spending pattern is detected, such as a sudden purchase in a foreign country, the system flags it for further investigation to prevent potential fraud.


• Healthcare Monitoring: Hospitals use anomaly detection to monitor patient vitals. For instance, if a patient's heart rate suddenly spikes or drops, the system alerts medical staff to take immediate action, potentially saving lives.

Additional Information

• Anomaly detection is also used in cybersecurity to identify unusual network activity that could indicate a breach.


• Retailers use anomaly detection to monitor inventory levels and detect potential theft or stock discrepancies.