How energy teams turn theft detection into governed action with Genie and AI business processes

Energy theft is the deliberate use of gas or electricity without paying for it, typically by tampering with a meter or supply so consumption goes unrecorded. Unlike a billing error or an unpaid bill, it involves physically modifying the connection, making it difficult to detect and potentially dangerous.

For revenue protection teams, theft is both a financial and a safety issue. Tampered meters and wiring can cause fires and gas leaks. Energy theft costs energy consumers in Great Britain alone over £1.4 billion a year, with only 40% of target cases being detected.

The challenge is no longer simply detecting more theft, as most companies already have ML models that can flag suspicious accounts. Teams need to unify revenue protection operations around a single governed workflow that accelerates the loop from flagged meter to recovered revenue to a safer household. Teams need to be able to interpret signals, prioritize investigations, prepare field teams, recover losses, and give leaders trusted metrics in a timely manner.

An ML insight is not yet a business outcome

Most organizations can train models that generate useful predictions, such as a churn probability, fraud score, failure forecast, or theft risk score. But the score itself is not the outcome. Value comes from what the business does next, and that is where many ML programs stall. The insight lands in a dashboard, while someone still has to translate it into action.