Gartner Market Guide for Online Fraud Detection
Online fraud detection is growing in complexity and demand, and its tools are being used for risk-based authentication and new account fraud prevention. Security and risk management leaders involved in online fraud detection should use machine-learning analytics and cloud-based deployment options.
These days, automated attacks, and the speed with which attackers can modify their techniques to avoid detection, continue to put pressure on rule-based systems. This slows detection of new attacks and increases false positives, as rule libraries expand in breadth and complexity trying to keep up with new fraudulent activity.
Market analysis, key findings, and recommendations cover:
How the online fraud detection market is evolving to meet the needs of security and risk management leaders
The rise of machine learning in fraud detection and an overview of machine learning models and their capabilities
Analysis of key vendor categories such as: fraud analytics; fraud hubs; contact center fraud prevention; endpoint and behavior analysis; and automated, remote and malware attack protection
Representative vendors in each of the above categories