Retail solutions
Fraud Detection for Retail
For retail chains considering fraud detection, the question is rarely "can it be done" — it's "can it be done in a way the business will actually adopt." That's where Beryl Analytics comes in.
Why retail teams choose Beryl Analytics for fraud detection
- Deep-domain models. Every fraud detection model we build is tuned to the realities of retail chains — not the synthetic benchmarks you see in vendor pitches.
- Production-ready, not throwaway. We ship pipelines, monitoring, alerting, and runbooks — the boring stuff that decides whether the system survives contact with reality.
- Operator-first design. Insights live inside the tools your team already uses, with thresholds and ownership matched to how decisions actually get made.
- Governance built in. Lineage, explainability, and access controls aren't an afterthought — they're scoped from day one and signed off with your security team.
- Outcomes measured in dollars. We track impact in revenue, cost avoided, or risk reduced — never in dashboard counts.
How we deliver fraud detection engagements
- 01
Discovery sprint (week 1)
Two days on-site with your operators to map the workflow, half a day with leadership to align on the dollar metric, and an afternoon writing the scope memo we'll work to.
- 02
Spike the riskiest assumption (weeks 2-3)
Before committing to the build, we attack the assumption most likely to kill the project — usually data availability or operator adoption. A negative result here saves months.
- 03
Build, in public (weeks 4-8)
Daily commits to a shared repo your engineers can read. Weekly demo to the operator group. Nothing is built in private.
- 04
Production cutover (weeks 9-10)
A planned cutover with a rollback plan, monitoring, and a human in the loop for the first fortnight. We don't walk away from cold launches.
Frequently asked questions about Fraud Detection for Retail
How long does a typical Fraud Detection engagement take for a retail business?
Most fraud detection projects for retail chains land a working production slice within 4-6 weeks, then harden and expand over the following 8-12 weeks. Larger retail 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 retail?
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 retail chains systems?
Yes. We're tool-agnostic and have integrated with Snowflake, BigQuery, Databricks, Salesforce, SAP, Oracle, custom in-house platforms, and dozens of retail-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 retail typically return 4-12x within the first year.
Do you work with retail 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 retail chains.