Intelligent Claims Automation for Insurance Provider
SecureCover Insurance
Client
SecureCover Insurance
Industry
InsurTech
Services Used
Machine Learning, AI Agents, Product Engineering
Technologies
Python, TensorFlow, Computer Vision, NLP
The Challenge
SecureCover Insurance, a regional property and casualty insurer processing over 1 million claims annually, faced mounting operational challenges. Average claims processing time was 14 days, with complex claims taking up to 30 days. The manual process required claims adjusters to review documents, assess damage, verify eligibility, and calculate settlements — all done by hand.
Operational costs for claims processing consumed 35% of premium revenue, well above industry benchmarks. Inconsistent decision-making across adjusters led to customer complaints and regulatory scrutiny. The company was losing market share to InsurTech competitors offering faster, digital-first experiences.
SecureCover needed to dramatically reduce processing time and costs while improving accuracy and customer satisfaction. The solution had to handle the full spectrum of claims — from simple auto glass replacements to complex property damage assessments.
Our Solution
Fastlab AI built an end-to-end intelligent claims automation platform that combined computer vision, NLP, and AI-agent orchestration. The system automated the entire claims lifecycle from first notice of loss (FNOL) through settlement.
For document processing, we built NLP models that extracted key information from claim forms, police reports, medical records, and repair estimates with 98% accuracy. A computer vision module analyzed damage photos to estimate repair costs for auto and property claims, validated against historical claims data.
We developed an AI agent orchestration layer that coordinated the claims workflow — verifying policy coverage, checking for fraud indicators, calculating reserves, and generating settlement recommendations. Simple claims (approximately 60% of volume) were processed end-to-end without human intervention.
Complex claims were routed to human adjusters with AI-generated summaries, recommended actions, and supporting analysis. The system included a fraud detection module that flagged suspicious patterns using network analysis and anomaly detection, catching 35% more fraudulent claims than the previous system.
Technologies Used
Python
TensorFlow
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Computer Vision
●
NLP
React
Node.js
PostgreSQL
AWS
Docker
Results
Measurable impact delivered
Faster Processing
Cost Reduction
Extraction Accuracy
Claims/Year
“Fastlab AI's claims automation platform has been a game-changer. Our customers now receive settlements in days instead of weeks. The ROI exceeded our most optimistic projections within six months of launch.”
Jennifer Walsh
Chief Operating Officer, SecureCover Insurance
Gallery
Selected screens and implementation highlights
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