How can utilities detect energy theft earlier without relying on manual investigation?

Bidgely detects potential energy theft by analyzing AMI and meter consumption data for anomalies that may indicate non-technical loss. Detection starts from the usage pattern rather than from a complaint, a billing review, or a field observation. Manual methods are slow and miss subtle patterns. Bidgely's anomaly-detection models analyze smart-meter consumption data and surface anomalous accounts for investigation. Investigations start from current account-level signals instead of lagging indicators.

How can utilities prioritize which theft cases are worth investigating first?

Bidgely prioritizes theft cases through AI-driven lead ranking rather than flat exception lists. Leads are ranked using signals such as recency, frequency, model confidence, and theft type, so investigators work the highest-confidence cases first. Analyst hours and crew capacity go to the accounts carrying the strongest risk signals.

How can utilities use AMI data to uncover suspicious consumption patterns?

Bidgely reads AMI data for changes in how an account consumes energy over time, identifying abnormal patterns across the meter base. Granular 15-minute and 30-minute interval data surfaces hidden anomalies, including the subtle ones manual review misses. What reaches the analyst is a customer-level lead, ready to enter an investigation workflow.

How can utilities reduce non-technical losses and protect revenue more effectively?

Bidgely reduces non-technical losses by detecting and ranking theft leads, then supporting lead assignment, field inspection, and revenue booking. Theft detection, revenue protection, and operational efficiency draw on the same anomaly-detection layer, so investigators, revenue protection teams, and field crews work from the same source of truth. Suspicious accounts surface early enough to act on before leakage compounds.

How can utilities make revenue protection field teams more efficient?

Bidgely makes revenue protection field work more efficient by ranking uninspected theft leads using factors such as recency, frequency, model confidence, and theft type. Field teams reach accounts with clearer signs of loss and spend less of the day on low-probability visits. Crews and analysts work the same ranking, so field priorities and investigation priorities do not diverge.

Theft Detection

Move from reactive detection to proactive prevention.

Stop chasing lagging indicators. Detect anomalies, prioritize the cases worth working, and protect revenue at scale.

Spot abnormal
consumption patterns.

Identify abnormal consumption patterns automatically — across the full meter base, without manual scanning.

Prioritize
high-risk accounts.

Rank accounts by AI-driven risk scoring so investigators focus on the cases most likely to be theft.

Reduce
non-technical losses.

Cut revenue leakage by detecting losses earlier and improving recovery rates across your service territory.

Make field teams
more efficient.

Improve hit rate per investigation and reduce cost per case by sending crews to higher-confidence leads.

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Detect Theft Early.
Prevent Losses Faster.

See AI-driven theft detection in action.