Fraud Analytics refers to the set of processes and technologies that analyze structured and unstructured data to identify suspicious patterns and behaviors indicative of fraudulent activities. These analytics harness historical data and real-time data streams to detect potential fraud before it results in significant losses. Key processes include anomaly detection, predictive modeling, and risk scoring, which are often enhanced through machine learning algorithms to adapt to evolving fraudulent techniques.