Machine learning is applied in fraud detection systems to analyze patterns and identify anomalies that indicate fraudulent activity. By using algorithms that learn from historical transaction data, these systems can flag unusual behavior or transactions in real-time. Techniques such as supervised learning, where models are trained on labeled data, and unsupervised learning, which identifies hidden patterns, are commonly used. Additionally, neural networks and other sophisticated models improve detection accuracy. These machine learning approaches help organizations minimize losses, reduce false positives, and enhance overall security.