Utilities solutions
Fraud Detection for Utilities
Most fraud detection initiatives stall on data quality, governance, or change management — not on the models themselves. Beryl Analytics solves the full stack for utility providers, from ingestion through to operator adoption.
Why utilities teams choose Beryl Analytics for fraud detection
- Decision-first scoping. Before we touch a model, we name the decision it will change, the owner, and the dollar metric. fraud detection that can't be tied back to one of those doesn't get built.
- Engineered observability. Every model ships with input drift detection, output distribution monitoring, and an alerting playbook. utility providers get systems that age gracefully.
- Boring tech where it matters. We default to the simplest model that meets the bar — gradient-boosted trees beat transformers far more often than vendors will admit.
- Pair-built, not handed over. Your engineers sit in every working session. They commit code. By go-live, the system is genuinely theirs.
- Honest post-mortems. Every engagement ends with a written read of what worked, what didn't, and what we'd tell utility providers to do next without us.
How we deliver fraud detection engagements
- 01
Data audit (week 1)
A focused review of what data you have, where it lives, and what shape it's in. Outputs a written read with the gotchas and where to start.
- 02
Contract & instrument (weeks 2-3)
We formalise the inputs the system will depend on — schemas, freshness SLAs, ownership — and instrument anything missing. No model without solid inputs.
- 03
Model + interface (weeks 4-7)
The model itself plus the surface your operators will actually use. Built together so the analysts who debug it know exactly what each output means.
- 04
Soft launch & calibration (weeks 8-10)
Live in a small slice of the business. We watch every decision the system informs, calibrate, and only then expand.
- 05
Full rollout
Scale to the full surface area with documentation, training, and an on-call playbook your team owns end-to-end.
Frequently asked questions about Fraud Detection for Utilities
How long does a typical Fraud Detection engagement take for a utilities business?
Most fraud detection projects for utility providers land a working production slice within 4-6 weeks, then harden and expand over the following 8-12 weeks. Larger utilities programmes that touch multiple business units take 4-6 months end-to-end.
What data do you need to start a Fraud Detection project in utilities?
Minimum viable inputs are 12-18 months of historical transactional or operational data, basic entity reference tables, and access to the systems that will consume the output. We can work with messy data — cleaning is part of the engagement.
Can Beryl Analytics integrate fraud detection with our existing utility providers systems?
Yes. We're tool-agnostic and have integrated with Snowflake, BigQuery, Databricks, Salesforce, SAP, Oracle, custom in-house platforms, and dozens of utilities-specific systems. Insights surface inside the tools your operators already use.
How do you measure success on a Fraud Detection engagement?
Before we model anything, we agree the business decision the output will change and the dollar metric we're targeting — revenue lifted, cost avoided, or risk reduced. Fraud Detection engagements in utilities typically return 4-12x within the first year.
Do you work with utilities businesses outside major NZ and AU cities?
Yes. We deliver remotely across New Zealand and Australia and visit on-site for discovery, key workshops, and go-live. Distance is not a blocker — many of our highest-impact fraud detection engagements have been with regional utility providers.