Operational Efficiency

Automated document processing for efficient insurance operations

In the insurance sector, processing high volumes of handwritten documents while maintaining accuracy remains a critical operational challenge. Agilytic partnered with a leading health insurer to implement automated document processing that could handle thousands of documents daily with near-perfect precision.

Automated document processing in health insurance
Automated document processing in health insurance
Automated document processing in health insurance

To protect confidentiality, we may alter specific details while preserving the accuracy of our core contribution.

Context & objectives

A leading health insurer processed thousands of documents manually each day. These handwritten documents contained numerous yes/no questions, but processing them efficiently while maintaining quality posed a significant challenge.

Seeking to streamline operations, the insurer aimed to automate document information extraction. Despite gradual improvements in their tools and processes over time, they required more sophisticated technology for full automated document processing.

Our key challenge was maximizing document coverage while maintaining near 100% precision. This was a critical requirement given the sensitive nature of subsequent decisions. The solution needed to:

  • Handle multiple document types with varying templates and checkboxes

  • Manage PDFs that contained missing, unordered, or extra pages

Approach

  1. System architecture and technology stack

The system takes scanned documents containing checkboxes as input and produces a summarized list of answers to the questions within these documents. We built the solution using:

  • Docker

  • AWS cloud development

  • PyTorch

  • OpenCV

Using carefully fine-tuned Natural Language Processing (NLP) techniques, we achieved precise text classification for automated document processing.

  1. Document recognition and processing workflow

The model follows these steps:

  1. Recognize the document's template automatically

  2. Compare and match with reference templates to find the highest possible correlation

  3. Match each page of the current PDF with its reference page, accounting for unordered or missing pages

  4. Detect checkboxes on each page automatically

  5. Use a Convolutional Neural Network (CNN) to classify boxes as checked, unchecked, or unclear

Results

Our integrated automated document processing application delivered key features that helped the insurer save time and resources by:

  • Providing more than 50% coverage and 100% precision for decision-making

  • Processing hundreds of documents per day

  • Accommodating diverse document types and datasets

  • Offering an intuitive interface suitable for all skill levels

The modular solution also ensures future scalability and flexibility with its hybrid cloud deployment capabilities. The client can easily expand the document processing pipeline, implement performance monitoring with automated alerts, and increase coverage across different process stages.

To safeguard confidentiality, we may modify certain details within our case studies.

Ready to reach your goals with data?

If you want to reach your goals through the smarter use of data and A.I., you're in the right place.

Ready to reach your goals with data?

If you want to reach your goals through the smarter use of data and A.I., you're in the right place.

Ready to reach your goals with data?

If you want to reach your goals through the smarter use of data and A.I., you're in the right place.

Ready to reach your goals with data?

If you want to reach your goals through the smarter use of data and A.I., you're in the right place.

© 2025 Agilytic

© 2025 Agilytic