Machine learning is utilized in fraud detection by analyzing large volumes of transaction data to identify anomalies and patterns indicative of fraudulent activity. Algorithms are trained on historical data to recognize typical behavior and flag transactions that deviate from this norm. The ability for these systems to continuously learn and adapt makes them effective at detecting new types of fraud, allowing organizations to respond swiftly. The integration of AI with machine learning enhances the accuracy and speed of detecting potential threats.