AI Revenue Protection Analytics
The Problem
“Detect and prevent utility revenue leakage at scale”
Organizations face these key challenges:
High volume of AMI and billing data overwhelms analysts; true issues are buried among false positives from simple threshold rules
Long lag between anomaly onset and detection leads to months of unbilled or underbilled energy and difficult-to-collect back-bills
Fragmented data and inconsistent processes across MDMS, CIS, billing, and field service create slow investigations and poor auditability
Impact When Solved
The Shift
Human Does
- •Review exception reports and customer histories to identify suspicious accounts
- •Prioritize investigations using static thresholds, analyst judgment, and reactive referrals
- •Coordinate field inspections and billing reviews across MDMS, CIS, billing, and field service
- •Validate findings, decide recovery actions, and document cases for audit and compliance
Automation
- •Apply basic rule-based flags for zero usage, sudden consumption drops, and billing exceptions
- •Generate periodic exception lists from AMI, meter event, and billing data
- •Surface limited account-level alerts based on predefined thresholds
Human Does
- •Approve investigation priorities and allocate field and billing review capacity
- •Review high-risk cases, confirm root cause, and decide customer, meter, or billing actions
- •Handle exceptions, disputed findings, and regulatory or audit-sensitive cases
AI Handles
- •Continuously score accounts for non-technical loss and billing defect risk using usage, events, tariffs, weather, and payment behavior
- •Detect anomalous consumption and billing patterns and identify emerging fraud or defect signals
- •Rank cases by likelihood, value at risk, and expected recovery to guide triage
- •Generate evidence packs with interval trends, event history, baseline comparisons, and billing validation checks
Operating Intelligence
How AI Revenue Protection Analytics 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 trigger customer action, field dispatch, or billing correction without review and approval from the responsible revenue protection, billing, or field operations lead [S2][S3].
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
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