Finance Case Study
Junction Financial
A gap analysis that saved a costly AI project from launching on unreliable data.
-22%False Positives
The Challenge
Where things stood before
Before working with us, Junction Financial was dealing with manual review processes that were both slow and inconsistent, and it was starting to cost them real business.
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.
What we ultimately delivered was a gap analysis that saved a costly AI project from launching on unreliable data, built specifically around what Junction Financial needed.
- Delivered within a 6-week timeline without cutting scope
- Benchmarked against clear, agreed-upon success metrics from day one
- Structured so Junction Financial's own team could monitor and maintain it going forward
- Designed around Junction Financial's actual data and workflow, not a generic template
Results
The impact, by the numbers
-22%
False Positives
-39%
Review Time
+32%
Detection Accuracy
97.2%
Model Explainability Score
Tech Stack
Built with
dbtSnowflakePythonSQL
Gravion Software
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