Machine learning plays a crucial role in fraud detection by analyzing vast amounts of transaction data to identify anomalies and suspicious patterns. By training algorithms on historical fraud data, machine learning models can continuously adapt and improve their detection capabilities, thus recognizing new fraudulent techniques in real-time. This proactive monitoring helps organizations reduce potential losses and enhance their overall security posture. Moreover, machine learning can differentiate between legitimate and illegitimate behaviors with high accuracy, providing businesses with valuable insights into consumer behavior and reducing false positives in fraud investigations.