Avidus Interactive

[ CLIENT OVERVIEW ]

A leading insurance provider sought to modernize and automate its manual document verification process. A critical challenge was verifying thumbprints on scanned documents submitted during policy issuance, especially in rural and semi-digital onboarding workflows.

[ THE CHALLENGES ]

Manual validation of thumbprints led to:

Delays in policy approvals and claims processing
Human error and fraud risk due to subjective verification
No scalable solution for matching scanned thumbprints to identity databases

The client needed an AI-based thumbprint detection and matching system that works seamlessly on scanned paper documents, even in varying quality formats.

[ Solution ]

We built Neutrinos, a custom ML-powered solution using TensorFlow, which detects thumbprints on uploaded scanned documents and matches them against an identity dataset.

[ KEY MODULES & FEATURES ]

Thumbprint Detection on Documents

Preprocessing using OpenCV to enhance fingerprint visibility
Region-based detection to locate thumbprint within a complex background
Automatically generate notices and resolutions.

Fingerprint Matching Using TensorFlow

Built a CNN (Convolutional Neural Network) trained on thumbprint datasets
Feature extraction for minutiae-based matching (ridge bifurcations, deltas, etc.)
Matching against a registered thumbprint database of users
Output: Match confidence score with success/fail threshold

Document Upload Interface

Integrated web portal for document upload (PDF, JPG, PNG)
Automated processing and real-time result dashboard

Scalability & Performance

Batch processing capability for bulk insurance documents
Optimized model inference for response time < 5 seconds per document

[ TECH STACK ]

Frontend

Backend

Database

Hosting

AI Layer

Development Framework

UI/UX

Integrations

[ THE IMPACT ]

93%+ detection accuracy for clean thumbprint scans
Reduced manual verification time from 3 mins to <10 seconds
Strengthened fraud prevention through biometric verification
Deployed successfully for rural agents onboarding in offline-first scenarios
System adaptable to other biometric traits (signatures, faces) if required

Manual validation of thumbprints led to:

[ THE OBJECTIVE ]

[ THE Outcome ]

Smart Thinking - Sharp Delivery