Finance Case Study
Junction Capital
A forecasting model that improved cash flow planning accuracy.
-30%False Positives
The Challenge
Where things stood before
When Junction Capital first reached out, manual review processes that were both slow and inconsistent was the issue sitting at the top of their list.
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.
The result was straightforward: a forecasting model that improved cash flow planning accuracy, built specifically around what Junction Capital needed.
- Built using Python and scikit-learn for a stable, maintainable foundation
- Designed around Junction Capital's actual data and workflow, not a generic template
- Validated thoroughly before deployment to avoid production surprises
- Benchmarked against clear, agreed-upon success metrics from day one
Results
The impact, by the numbers
-30%
False Positives
-47%
Review Time
+32%
Detection Accuracy
94.4%
Model Explainability Score
Tech Stack
Built with
Pythonscikit-learnSQLMLflow
Gravion Commerce
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