Real-Time Underwriting Risk and Fraud Analytics for an Embedded Patient-Financing Provider
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Problem
Slow approvals, rising losses
A patient-financing fintech embedded in roughly 60 dental offices approved treatment plans at the front desk using a manual rules checklist. Decisions took several minutes while patients waited, borderline applications defaulted to rejection, and identity fraud was slipping through unnoticed. Delinquency at 90 days had climbed above 9%, squeezing margins on every funded plan.
Solution
Real-time risk scoring engine
We built a real-time underwriting engine combining a gradient-boosted repayment-risk model trained on historical plan performance with a parallel fraud layer scoring device, identity and velocity signals. A decisioning API returns approve, decline or refer in under 20 seconds, borderline cases route to a human review queue, and drift and portfolio dashboards keep both models continuously monitored.
Measurable Impact
What changed after launch
Median point-of-sale decision time cut from over 4 minutes to under 20 seconds
90-day delinquency reduced from 9.1% to 6.4% at an unchanged overall approval rate
Confirmed identity-fraud losses down 38% across the first two quarters after launch
Manual underwriting reviews narrowed to 7% of applications via the referral queue
Tech & Tools Used
What powered the build
Ready to Build your Healthcare Fintech Business with Risk & Fraud Analytics
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