AI Non-Technical Loss Detection
The Problem
“Detect and reduce non-technical losses at scale”
Organizations face these key challenges:
Low inspection hit-rates and high false positives drive wasted truck rolls and investigator time
Data silos across AMI, CIS/billing, meter events, and network/feeder data delay detection and obscure root causes
Evolving theft tactics (bypass, magnetic tamper, meter programming, illegal reconnections) outpace static rules and manual heuristics
Impact When Solved
The Shift
Human Does
- •Review billing, meter, and customer records for suspicious usage patterns
- •Apply rule-based flags and feeder balance checks to identify possible NTL cases
- •Select accounts and locations for field inspection based on analyst judgment and complaints
- •Conduct site inspections and document theft, tampering, or billing irregularities
Automation
Human Does
- •Approve inspection priorities and allocate field resources based on risk and expected recovery
- •Review high-risk cases with explanations before dispatch or enforcement action
- •Handle exceptions, disputed cases, and sensitive customer or regulatory decisions
AI Handles
- •Analyze consumption, meter events, payment behavior, and feeder context to score NTL risk
- •Prioritize accounts and locations by likelihood of theft, recoverable value, and visit cost
- •Generate case explanations and supporting risk factors for investigator review
- •Monitor input quality, prediction patterns, and model drift for changes in behavior
Operating Intelligence
How AI Non-Technical Loss Detection 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 investigator or field operations manager approval [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 Non-Technical Loss Detection implementations:
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