Insurance solutions
Anomaly Detection for Insurance
Anomaly detection only generates compounding returns when it's wired into the daily workflows of insurers. Beryl Analytics embeds with your teams to ship analytics that change decisions, not just charts.
Why insurance teams choose Beryl Analytics for anomaly detection
- Built for compounding value. Each anomaly 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 anomaly detection engagements
- 01
Frame the decision
Before we touch a model, we agree what decision the output will change, who owns that decision, and what counts as success in dollars or risk reduced.
- 02
Land a working slice
A narrow but complete production system: source-to-decision in 4-6 weeks, monitored, owned, and measurable. Then we expand from real evidence.
- 03
Embed the operating model
Retraining cadence, alerting thresholds, escalation runbooks, and clear ownership. The system stops being "the analytics project" and becomes part of how the business runs.
- 04
Compound the wins
Reuse the foundation across the next use case. Each engagement makes the next cheaper, faster, and lower-risk.
Frequently asked questions about Anomaly Detection for Insurance
How long does a typical Anomaly Detection engagement take for a insurance business?
Most anomaly 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 Anomaly 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 anomaly 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 Anomaly 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. Anomaly 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 anomaly detection engagements have been with regional insurers.