AI Energy Theft Prevention
The Problem
“Detect and prevent electricity theft at scale”
Organizations face these key challenges:
High non-technical losses from meter tampering, bypassing, illegal connections, and billing manipulation, concentrated in specific feeders and customer segments
Inefficient field operations due to low hit-rates, manual triage, and limited investigator capacity, leading to high cost per confirmed case
Limited visibility into evolving theft tactics and coordinated fraud (e.g., intermittent bypassing, shared illegal taps), causing repeated losses and safety/reliability risks
Impact When Solved
The Shift
Human Does
- •Review exception reports, customer complaints, and audit findings to identify suspicious accounts
- •Prioritize inspections using rules, thresholds, and investigator judgment
- •Dispatch field crews to inspect meters, service lines, and suspected illegal connections
- •Confirm theft cases, decide recovery actions, and document enforcement outcomes
Automation
- •Generate basic exception flags from billing anomalies such as zero usage, sudden drops, or broken seals
Human Does
- •Approve investigation priorities and allocate field inspections based on AI-ranked risk cases
- •Review high-risk or ambiguous cases and decide escalation, customer action, or enforcement steps
- •Validate confirmed theft findings from field evidence and authorize recovery actions
AI Handles
- •Continuously score accounts, meters, transformers, and feeders for theft risk using usage, billing, payment, and event patterns
- •Detect anomalies, neighborhood clusters, and meter-to-feeder inconsistencies that indicate possible theft or fraud rings
- •Prioritize and triage cases into ranked investigation queues with recommended next actions
- •Monitor post-action outcomes and refresh risk rankings to surface repeat offenders and emerging theft patterns
Operating Intelligence
How AI Energy Theft Prevention runs once it is live
AI surfaces what is hidden in the data.
Humans do the substantive investigation.
Closed cases sharpen future detection.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Scan
Step 2
Detect
Step 3
Assemble Evidence
Step 4
Investigate
Step 5
Act
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI scans and assembles evidence autonomously. Humans do the substantive investigation. Closed cases improve future scanning.
The Loop
6 steps
Scan
Scan broad data sources continuously.
Detect
Surface anomalies, links, or emerging signals.
Assemble Evidence
Pull related records into a working case file.
Investigate
Humans interpret evidence and make case judgments.
Authority gates · 1
The system must not dispatch field inspections or enforcement actions without review and approval from revenue protection or field inspection leadership. [S1]
Why this step is human
Investigative judgment involves ambiguity, legal considerations, and stakeholder impact that require human expertise.
Act
Carry out the human-directed next step.
Feedback
Closed investigations improve future detection.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in AI Energy Theft Prevention implementations:
Real-World Use Cases
AI emergency scenario simulation for nuclear plant response planning
AI runs thousands of nuclear emergency what-if drills on a computer and helps choose the best response before a real problem happens.
AI-assisted grid congestion management
Use AI to help power-grid operators spot and manage overloaded parts of the grid before they become bigger problems.
AI Power Grid Congestion Management
This AI system helps manage electricity grid congestion by optimizing the layout and connections of the grid, reducing costs and emissions.