Fintech Case Study
Junipoint Financial
A domain-adapted model that significantly improved accuracy on specialized terminology.
-17%False Positives
The Challenge
Where things stood before
Junipoint Financial came to us with a problem that had been building for a while: manual review processes that were both slow and inconsistent.
The problem was quietly getting more expensive to ignore every month it went unaddressed.
The Solution
What we built
Rather than jumping straight to a model, we spent time understanding the specific shape of the problem first.
What we ultimately delivered was a domain-adapted model that significantly improved accuracy on specialized terminology, built specifically around what Junipoint Financial needed.
- Structured so Junipoint Financial's own team could monitor and maintain it going forward
- Validated thoroughly before deployment to avoid production surprises
- Built using Hugging Face and PyTorch for a stable, maintainable foundation
- Benchmarked against clear, agreed-upon success metrics from day one
Results
The impact, by the numbers
-17%
False Positives
-46%
Review Time
+55%
Detection Accuracy
97.1%
Model Explainability Score
Tech Stack
Built with
Hugging FacePyTorchLoRAPython
Kettlewell Retail Group
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