Finance Case Study
Novasound Financial
A POC that tested three approaches and identified the clear technical winner.
-18%False Positives
The Challenge
Where things stood before
Before working with us, Novasound Financial was dealing with manual review processes that were both slow and inconsistent, and it was starting to cost them real business.
Competitors and peers seemed to have this figured out already, which only made it more frustrating.
The Solution
What we built
We started by getting a clear picture of exactly where things were breaking down before proposing anything.
From there, a POC that tested three approaches and identified the clear technical winner, built specifically around what Novasound Financial needed.
- Structured so Novasound Financial's own team could monitor and maintain it going forward
- Built using Python and PyTorch for a stable, maintainable foundation
- Designed around Novasound Financial's actual data and workflow, not a generic template
- Validated thoroughly before deployment to avoid production surprises
Results
The impact, by the numbers
-18%
False Positives
-35%
Review Time
+53%
Detection Accuracy
98.8%
Model Explainability Score
Tech Stack
Built with
PythonPyTorchOpenAI APIJupyter
Kestrelworks Software
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