Applications of AI for Anomaly Detection
This IDC Perspective examines the business benefits of AI-powered anomaly detection in organizations. It discusses the various techniques organizations use to identify anomalies and the most prevalent use cases. It also discusses some of the key players in this industry. It provides guidance to technology buyers on how to embrace AI-powered anomaly detection and maximize ROI."AI-powered anomaly detection is growing in popularity and has numerous applications across business activities and industries," says Raghunandhan Kuppuswamy, research manager for AI and Automation at IDC. "Choosing the correct AI-powered anomaly detection solutions could eliminate many of the problems associated with anomalies and the issues companies encounter as a result of anomalies."
Please Note: Extended description available upon request.
- Executive Snapshot
- Situation Overview
- AI-Powered Anomaly Detection
- What Are the Various Anomaly Detection Techniques?
- Supervised Anomaly Detection
- K-Nearest Neighbors
- Z-Score
- Decision Tree Algorithm
- Support Vector Machines
- Bayesian Networks
- Unsupervised Anomaly Detection
- K-Means Clustering
- Autoencoders
- Isolation Forest
- Local Outlier Factor
- Semi-Supervised Anomaly Detection
- Anomaly Detection on Edge Devices
- Use Cases
- AI-Powered Anomaly Detection in Banking
- AI-Powered Anomaly Detection in Cybersecurity
- AI-Powered Anomaly Detection in Healthcare
- Companies to Watch in the AI-Powered Anomaly Detection Space
- Advice for the Technology Buyer
- Learn More
- Related Research
- Synopsis
Research Assistance
Learn how to effectively navigate the market research process to help guide your organization on the journey to success.
Download eBook