Applications of outlier detections

1. Fraud detection: fraudulent applications for credit cards, state benefits or fraudulent usage of credit cards or mobile phones. 
2. Loan application processing: fraudulent applications or potentially problematical customers. 
3. Intrusion detection, such as unauthorized access in computer networks. 
4. Activity monitoring: for instance the detection of mobile phone fraud by monitoring phone activity or suspicious trades in the equity markets. 
5. Network performance: monitoring of the performance of computer networks, for example to detect network bottlenecks. 
6. Fault diagnosis: processes monitoring to detect faults for instance in motors, generators, and pipelines. 
7. Structural defect detection, such as monitoring of manufacturing lines to detect faulty production runs. 
8. Satellite image analysis: identification of novel features or misclassified features. 
9. Detecting novelties in images (for robot neo-taxis or surveillance systems). 
10. Motion segmentation: such as detection of the features of moving images independently on the background. 
11. Time-series monitoring: monitoring of safety critical applications such as drilling or high-speed milling. 
12. Medical condition monitoring (such as heart rate monitors). 
13. Pharmaceutical research (identifying novel molecular structures). 
14. Detecting novelty in text. To detect the onset of news stories, for topic detection and tracking or for traders to pinpoint equity, commodities. 
15. Detecting unexpected entries in databases (in data mining application, to the aim of detecting errors, frauds or valid but unexpected entries). 
16. Detecting mislabeled data in a training data set.

 Reference:

Hodge, V.J. (2004), A survey of outlier detection methodologies, Kluver Academic Publishers, Netherlands, January 2004.