Insurance solutions
Fraud Detection for Insurance
If you've ever had a data initiative die in handover, you know the problem isn't the model — it's the moment the consultants leave. Beryl Analytics pairs into insurers from day one so the system runs itself before we step back.
Why insurance teams choose Beryl Analytics for fraud detection
- Built for compounding value. Each fraud detection engagement leaves insurers with infrastructure that accelerates the next one — shared feature stores, reusable pipelines, documented data contracts.
- Real handover. We pair your team into the build from day one. By go-live, they own the system. We're optional from then on.
- Practical AI. We've shipped LLM-augmented analytics where they help, and stayed with simpler models where they outperform. Hype is not a strategy.
- Audit-friendly. Every model decision is traceable. Compliance and risk teams stop blocking — they start enabling.
- Track record. 1,000+ models in production. Across heavy-industry, regulated, and consumer domains.
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 Insurance
How long does a typical Fraud Detection engagement take for a insurance business?
Most fraud detection projects for insurers land a working production slice within 4-6 weeks, then harden and expand over the following 8-12 weeks. Larger insurance 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 insurance?
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 insurers systems?
Yes. We're tool-agnostic and have integrated with Snowflake, BigQuery, Databricks, Salesforce, SAP, Oracle, custom in-house platforms, and dozens of insurance-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 insurance typically return 4-12x within the first year.
Do you work with insurance 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 insurers.