Machine learning can be used in fraud detection by analyzing patterns and anomalies in transaction data to identify potentially fraudulent activities. Algorithms can be trained on historical data to recognize legitimate behaviors and flag irregular transactions for further investigation. This proactive approach enhances security measures, reducing financial losses for businesses and consumers. Machine learning models can adapt to new fraud tactics over time, continuously improving detection accuracy and helping organizations remain resilient against evolving threats.