Retail Case Study
Gravion Commerce
A fraud detection system that reduced false positives while catching more real fraud.
+56%Conversion Rate
The Challenge
Where things stood before
Gravion Commerce came to us with a problem that had been building for a while: manual processes that couldn't scale with a growing catalog and customer base.
It was the kind of issue that showed up in day-to-day frustration long before it showed up in a report.
The Solution
What we built
Our approach focused on the root cause rather than the most visible symptom.
From there, a fraud detection system that reduced false positives while catching more real fraud, built specifically around what Gravion Commerce needed.
- Built using Python and scikit-learn for a stable, maintainable foundation
- Delivered within a 7-week timeline without cutting scope
- Benchmarked against clear, agreed-upon success metrics from day one
- Designed around Gravion Commerce's actual data and workflow, not a generic template
Results
The impact, by the numbers
+56%
Conversion Rate
+30%
Forecast Accuracy
-33%
Manual Review Time
+33%
Revenue Lift
Tech Stack
Built with
Pythonscikit-learnSQLMLflow
Highmoor Technologies
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