A high-end telecom Giant in US approached GBee with a requirement to identify anomalies in the broadband usage pattern. The company was facing severe financial hit due to inaccurate usage reporting, which was caused by unintended duplication of individual usage records and suspicious activities on user accounts. The client wanted to assess the usage trend of their enormous customer base of approx. 50 million customers and detect any deviation in their normal usage pattern on a real-time basis.

The GBee platform was configured to ingest real time high-speed usage data for individual customers. The framework transformed the raw data into rich data using proprietary features such as classification, time and space context, scaling and grading. The enriched data was fed to Knowledge engine which leveraged Auto ML features and identified anomalies in usage pattern for deriving consumable intelligence. GBee was able to construct an end-to-end machine learning pipeline that helped mitigate revenue losses for one of the largest telecom service provider in USA.