Machine learning algorithms enhance fraud detection systems by analyzing vast amounts of transaction data to identify patterns and anomalies indicative of fraudulent behavior. These algorithms can learn from historical data, identifying characteristics that distinguish legitimate transactions from suspicious ones. By employing techniques like anomaly detection and classification, machine learning models can continuously improve their accuracy over time. Moreover, real-time processing enables swift responses to potential fraud, reducing losses for financial institutions. As these systems evolve, they become increasingly effective at adapting to new fraud tactics, thereby strengthening overall security measures.