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Manual Thumbprint Checks Were Slowing Down Every Policy Approval
CLEINT :
Neutrinos: Insurance Provider
Location :
India
93%+
Thumbprint detection accuracy on scanned documents
3 min → <10 sec
Verification time per document
100%
AI-driven, eliminating subjective manual checks
Rural-Ready
Scalable onboarding across high-volume agent networks
Problem
Manual thumbprint verification on scanned policy documents was slow, inconsistent, and prone to fraud and human error. The insurer needed a biometric system that could accurately detect and match thumbprints across varying scan qualities, reliable enough for high-volume, rural, and offline-first agent onboarding.
What we built
Neutrinos, a custom AI/ML-powered identity verification system combining computer vision and deep learning. OpenCV-based preprocessing handles noisy, skewed, and low-quality scans, while a region-based detector locates thumbprints within complex document layouts. A CNN built on TensorFlow performs minutiae-based matching, surfaced through a secure upload portal and a real-time verification dashboard with confidence scores, supporting both single-policy checks and batch processing.
"Verification used to be our biggest bottleneck — three minutes per document, with room for human error at every step. Neutrinos gave us seconds, not minutes, and a level of accuracy we couldn't get manually, even in our most rural onboarding channels."
Neutrinos Team
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