Our client is a casualty insurance provider operating across multiple markets with a broad portfolio of personal and commercial policies. Their claims department handled thousands of incoming claims per week, including auto accidents, property damage, and general liability.
The existing claims process was largely manual and prone to inefficiencies. High volumes of unstructured data made automation difficult, and adjusters spent significant time cross-referencing policies, claim history, and supporting documents.
We designed and deployed an end-to-end validation engine for claims processing that intelligently analyzed incoming claims, verified documentation, assessed fraud risk, and prioritized them for either automatic approval or human review.
Software Deployment
Claims Mapping and Use Case Prioritization
We started by collaborating with claims handlers and fraud teams to map the full lifecycle of a claim. We identified high-frequency claim types, common fraud indicators, and key pain points where automation could deliver the most value.
Document Collection and Annotation
We gathered anonymized claims files, including forms, handwritten notes, invoices, emails, and photo evidence. Our team created labeled datasets for document classification, field extraction, and anomaly identification.
AI Model Training and Validation Framework
We developed a modular system consisting of document parsers, rule-based validators, and anomaly detection models. OCR and LLMs handled unstructured inputs, while our logic engine cross-referenced extracted data with internal policy rules and historical claims databases.
Controlled Launch and Iterative Feedback
We deployed the system to a subset of claims teams handling high-volume but low-risk cases. Human reviewers ran the system in parallel with their standard process to validate accuracy and ease of use.
Full Deployment and Continuous Learning
After proving consistent performance, the system was scaled to process claims across all business lines. Monitoring dashboards were introduced to track model accuracy, decision speed, and auto-approval rates.
The engine could process PDFs, scanned forms, handwritten notes, emails, images of damage, invoices, and third-party reports.
The system automatically cross-referenced extracted claim data with the policyholder’s coverage terms, prior claims, and known risk rules.
Each claim received a confidence score based on document completeness, consistency, fraud risk indicators, and historical patterns.
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