Insurance - Health & General Claims

Challenges

• High manual workload due to mixed-format documents, including scanned and handwritten claims.
• Inconsistent or incomplete data causing delays in internal verification.
• Long claim processing times, often taking days, impacting customer satisfaction.
• Need for high accuracy in extracting sensitive personal and medical information.
• Scalability concerns, as claim volumes increased steadily over time.

What We Did

• Built an AI document processing engine with full OCR capabilities for PDFs, scanned images, and handwritten forms.
• Automated key information extraction, including medical details, personal info, and claim-specific fields.
• Implemented data validation and verification checks to highlight missing or inconsistent information.
• Integrated the AI engine into the client’s claim workflow, enabling faster approvals and reduced errors.
• Provided monitoring and reporting tools to track extraction accuracy and operational efficiency.

Key Features

1. AI-Powered Document Extraction

• Automatically extracts key fields from PDFs, scanned files, and handwritten forms
• Supports medical details, personal information, claim amounts, diagnosis, billing codes, etc.
• OCR + NLP pipeline for high-accuracy structured data extraction

2. Full Multi-Format Document Support

• PDF documents
• Scanned images (JPEG, PNG, TIFF)
• Handwritten claim forms
• Multi-page claim files

3. Intelligent Data Validation

• Flags missing fields, inconsistent values, and incomplete documents
• Highlights extracted text vs. ground truth for quick human verification
• Reduces back-and-forth communication with customers

4. Automated Claim Classification

• Categorizes claim type: health, accident, general insurance, etc.
• Routes claims to the correct internal workflow

5. Workflow Integration

• Seamless integration with internal claim systems (CRM, Claims Platform, DMS)
• Auto-sync extracted data into backend systems for faster processing

6. Accuracy Monitoring Dashboard

• Real-time accuracy tracking
• Model performance monitoring
• Error heatmaps for continuous model improvements

Impact

• 40–50% reduction in manual effort for claim processing.
• Claim processing time reduced to under 8 hours, down from multiple days.
• Data extraction accuracy improved to approximately 90%, reducing errors and rework.
• Backlog volume reduced by 30–35% within the first quarter.
• Enhanced operational efficiency and customer satisfaction, while enabling the client to scale claim processing without adding headcount.