Machine learning enhances fraud detection systems by analyzing vast amounts of transaction data in real-time to identify patterns indicative of fraudulent activity. It employs algorithms that adapt and improve over time through training on historical data, allowing organizations to detect anomalies that traditional methods might miss. By automating this process, companies can reduce false positives and respond promptly to suspicious actions, ensuring a more effective defense against financial losses.