Anomaly Detection

A database includes a plurality of physical entity records of insurance claims, and each physical entity record includes physical data values for at least one numeric attribute and partition-specifying values concerning values for one or more nominal attributes from one or more insurance claim records. A method for detecting anomalies in the database includes retrieving and partitioning the plurality of physical entity records from the database, training an unsupervised anomaly detection algorithm on the plurality of physical entity records to obtain a trained anomaly detection model for each partition, calculating an anomaly score for each physical entity record using the trained anomaly detection model associated with each partition, and updating the plurality of physical entity records in the database by adding the associated anomaly score. The method is used to determine if a user-provided physical entity record is fraudulent using the anomaly score.