THE SOLUTION
Huna applies advanced machine learning techniques to historical measurement data and low-voltage electrical models to identify anomalous behavioral patterns. Using a sufficient volume of historical data, the system trains specific models focused on the early detection of fraud and tampering in smart meters.
Identifies deviations from expected behavior and automatically classifies cases according to their probability of fraud. By combining consumption patterns, electrical context, and temporal consistency, the platform significantly reduces false positives and generates prioritized, traceable reports that streamline and support decision-making.
Deployment is carried out independently on existing infrastructure, without requiring integration with the ADMS, enabling agile implementation with minimal operational impact. In addition, the generated information can be tailored to each organization’s needs through custom reports or integrations with corporate platforms, databases, and APIs.
