Supply Chain solutions
Fraud Detection for Supply Chain
Most fraud detection initiatives stall on data quality, governance, or change management — not on the models themselves. Beryl Analytics solves the full stack for supply chain operators, from ingestion through to operator adoption.
Why supply chain 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. supply chain operators 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 supply chain operators to do next without us.
How we deliver fraud 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 Fraud Detection for Supply Chain
How long does a typical Fraud Detection engagement take for a supply chain business?
Most fraud detection projects for supply chain operators land a working production slice within 4-6 weeks, then harden and expand over the following 8-12 weeks. Larger supply chain 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 supply chain?
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 supply chain operators systems?
Yes. We're tool-agnostic and have integrated with Snowflake, BigQuery, Databricks, Salesforce, SAP, Oracle, custom in-house platforms, and dozens of supply chain-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 supply chain typically return 4-12x within the first year.
Do you work with supply chain 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 supply chain operators.