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

2–4x improvement in theft detection hit-rate by prioritizing highest-risk accounts and locations0.5–2.0 percentage point NTL reduction, translating to ~$0.6M–$12M+ annual revenue recovery depending on utility size and tariffs30–50% reduction in cost per confirmed case via fewer unnecessary site visits and better investigator productivity

The Shift

Real-World Use Cases

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5

AI Grid Congestion Management

This AI helps optimize the layout of power grids to reduce congestion without increasing costs or carbon emissions.

optimizationgrowing

8.0