Machine learning plays a pivotal role in real-time fraud detection by analyzing vast datasets to identify anomalies and potential fraudulent behavior. Algorithms are trained on historical transaction data to recognize patterns associated with legitimate activity versus fraud. As new transactions occur, these systems can instantly evaluate their risk levels, triggering alerts or automated responses if suspicious activity is detected. This enables organizations to mitigate fraud risk promptly, protecting their assets and customers from financial losses.