Machine learning automates fraud detection by analyzing vast amounts of transactional data to identify unusual patterns and anomalies. Algorithms are trained using historical data, learning to distinguish between legitimate and fraudulent behaviors. Techniques such as supervised learning can provide models that adapt based on newly acquired data. Incorporating real-time analysis allows organizations to detect fraudulent activities almost instantly, reducing potential losses. This automation enhances efficiency and accuracy, enabling companies to focus resources on maintaining security and compliance.