Case study · From the Pluris libraryReceipt no. 9843
What sucked
A large-scale fintech/proptech platform processing hundreds of document applications a day flagged a large share for human review.
Manual verification consumed reviewer time, and existing tools produced high false-positive rates that drove unnecessary escalations, all without the option to add headcount.
What they built
An interpretable ML fraud-detection system for document verification, a Pass/Fail/Caution classifier on 65+ engineered features, with a plain-English 'AI Assist' explanation layer and a feedback-driven retraining pipeline.
What changed
99% Precision
In production fraud detection.
1.2% False-Positive Rate
Roughly 10x better than the LLM and baseline tools benchmarked.
~50% Less Manual Review
Escalation workload roughly halved.
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