Intelligent Claims Processing

Claims processing is slowed by manual intake, fragmented validation, and subjective damage assessment. Inconsistent data and workflows increase cycle time, cost, leakage risk, and customer dissatisfaction.

Insurance claims often require manual document intake, repeated data entry, and time-consuming validation across policy, coverage, and supporting evidence. Critical information arrives in unstructured formats, leading to inconsistent capture and downstream rework. Damage assessment from photos is frequently subjective and varies by adjuster experience, contributing to delays and variable outcomes. Rule and policy validation is fragmented across systems, and settlement decisions can lack consistent rationale and traceability. These challenges inflate cycle times and operational costs, increase leakage exposure, and reduce customer satisfaction, especially during peak-event surges when claim volumes spike.

Automate claim intake, document extraction, and damage assessment using AI-driven workflows. Generate guided settlement recommendations with policy validation, confidence scoring, and audit-ready traceability.

The solution automates claims processing by capturing claim submissions and documents, extracting key fields, and normalizing data into a structured claim record. AI models assess damage from images to identify impacted parts and severity, while a validation layer applies policy and business rules to confirm coverage and flag exceptions. The workflow generates settlement recommendations with confidence indicators and clear drivers, enabling straight-through processing for low-complexity claims and routing exceptions for human review. All artifacts—documents, extracted data, assessments, and decisions—are securely stored with full traceability to support compliance, audit needs, and seamless handoff to claims teams.

Accelerates claim resolution by automating intake and assessment, improving decision consistency, and reducing manual effort and leakage risk.

Automates data capture and damage assessment to reduce claim handling time and speed up resolution for eligible claims.

Applies standardized rules and AI-driven severity assessment to reduce adjuster variability and improve settlement consistency.

Reduces manual re-keying, validation work, and rework through structured records, exception routing, and guided processing workflows.

Automates extraction of critical claim fields from uploaded documents and forms, normalizes them into a structured claim record, and reduces manual re-keying and downstream rework. The workflow supports multi-format inputs while maintaining traceability of extracted values back to source evidence for governance and audit needs.

Assesses damage from images to identify impacted components and severity drivers, then combines results with policy validation to generate settlement guidance and confidence indicators. Supports straight-through processing for low-complexity claims and routes exceptions for human review with clear rationale and supporting artifacts.

Brillio modernizes claims processing on AWS by automating intake, document and image analysis, and guided decisioning. Using AWS AI and serverless orchestration, the solution accelerates low‑complexity claim settlements, routes exceptions for human review, ensures auditability and compliance, and scales efficiently during catastrophe-driven spikes in claim volume.

AWS Native Services: Amazon Bedrock, AWS Amplify, Aamzon API Gateway, AWS Cognito, AWS SDK

A large national insurer experienced long claim cycle times due to manual intake, repeated re-keying, and inconsistent damage assessment from images. Policy validation was fragmented across systems, creating rework, variable outcomes, and limited traceability for audits and customer disputes.

Brillio deployed an AWS-based claims workflow that automated document extraction, image-based damage assessment, and policy/rule validation. The system generated guided settlement recommendations with confidence scores and routed exceptions to adjusters with all supporting evidence.

The insurer reduced time spent on intake and assessment, improved consistency of claim outcomes, and increased adjuster productivity by focusing human review on exceptions. Audit readiness improved through standardized decision traces and secure storage of artifacts and outputs.