Machine learning is increasingly adopted in fraud detection to analyze patterns and anomalies in large datasets. By using algorithms that learn from historical transaction data, machine learning models can identify suspicious behaviors in real-time, increasing the likelihood of catching fraudulent activities before they escalate. These models continuously improve their accuracy over time through training on new data. As a result, machine learning brings a proactive approach to fraud detection, providing organizations with a better defense against fraud and a way to save costs related to losses.