Abstract: Systems and methods are provided for performing anomaly detection. One example method relates to transaction data including fraud scores output by a fraud score model generated by a machine learning system. The method includes determining, by a computing device, divergence values for multiple segments of payment accounts between baseline distributions of fraud scores and current distributions of fraud scores for the segments and detecting, by the computing device, at least one of the divergence values for at least one of the multiple segments as an anomaly. The method also includes categorizing, by the computing device, the detected anomaly into one of multiple categories, whereby the one of the multiple categories is indicative of a type of issue associated with the detected anomaly.