Abstract: One or more computing devices, systems, and/or methods for identifying anomalous behavior of users are provided. In an example, users of a telecommunication service provider may be segmented into a plurality of user segments based upon telecommunication service metrics associated with the users. A machine learning model may be trained using telecommunication service information associated with users of the first user segment to generate a trained machine learning model. Using the trained machine learning model, a forecast of telecommunication service usage associated with a first user segment of the plurality of user segments. A telecommunication service usage metric, associated with a user belonging to the first user segment, may be compared with a range indicated by the forecast. The user may be flagged as having anomalous behavior based upon a determination that one or more telecommunication usage metrics, associated with the user, are outside one or more ranges indicated by the forecast.

Abstract: One or more computing devices, systems, and/or methods for identifying anomalous behavior of users are provided. In an example, users of a telecommunication service provider may be segmented into a plurality of user segments based upon telecommunication service metrics associated with the users. A machine learning model may be trained using telecommunication service information associated with users of the first user segment to generate a trained machine learning model. Using the trained machine learning model, a forecast of telecommunication service usage associated with a first user segment of the plurality of user segments. A telecommunication service usage metric, associated with a user belonging to the first user segment, may be compared with a range indicated by the forecast. The user may be flagged as having anomalous behavior based upon a determination that one or more telecommunication usage metrics, associated with the user, are outside one or more ranges indicated by the forecast.

Abstract: One or more computing devices, systems, and/or methods for identifying anomalous behavior of users are provided. In an example, users of a telecommunication service provider may be segmented into a plurality of user segments based upon telecommunication service metrics associated with the users. A machine learning model may be trained using telecommunication service information associated with users of the first user segment to generate a trained machine learning model. Using the trained machine learning model, a forecast of telecommunication service usage associated with a first user segment of the plurality of user segments. A telecommunication service usage metric, associated with a user belonging to the first user segment, may be compared with a range indicated by the forecast. The user may be flagged as having anomalous behavior based upon a determination that one or more telecommunication usage metrics, associated with the user, are outside one or more ranges indicated by the forecast.