Confidential insurance company streamlines email triage for claims processing

Use case typeClaims automationUpdated Jun 13, 2026

A major insurance company faced inefficiencies due to the high volume of incoming claims emails, leading to slow routing and heavy manual workloads for claims teams. They implemented an automated email triage solution using Azure AI Language Studio and Power Platform to classify and route emails. The system extracts policy numbers via Named Entity Recognition (NER) for accurate department assignment. If no policy number is found, text classification with Azure AI determines the department. Integrated with Dynamics for case management and Power Automate for workflow, the solution reduced manual intervention, improved accuracy, and sped up customer responses. Email extraction and department routing were fully automated, and continuous monitoring enabled ongoing model improvements. This low-code approach minimized the need for custom development, allowing staff to update classification models as business needs evolved. Feedback loops and active model retraining were instituted to adapt to new email trends. Automated sentiment analysis and prioritization further optimized the workflow.

Industry
Insurance
Location
Global
Published
June 2024

Reported outcomes

Strategic outcomes

Speed & agilityAutomated email triage and routingCustomer experience & trustImproved responsiveness to customersBetter decisions & insightImproved routing accuracyScale & capacityReduced manual processing workload

Primary read

Use case focus

Showing 3 of 3

  • 1Automated Email Classification and Routing for Claims
  • 2Named Entity Recognition for Policy Number Extraction
  • 3Department Assignment via NLP Text Classification
  • High volume of claims emails causing manual routing bottlenecks.
  • Delays in triage reduced responsiveness to customers.
  • Frequent misclassification led to incorrect assignment of cases.
  • Claims processing teams experienced stress and backlogs.
  • Manual extraction of policy numbers was error-prone and inefficient.
  • Implemented automated email reception and registration in Dynamics using Dataverse.
  • Used Azure AI Language Studio's NER to extract policy numbers from emails.
  • Applied text classification in Azure AI Language Studio to determine department when no policy number is found.
  • Integrated Power Automate for workflow automation and assignment in Dynamics case management.
  • Continuous feedback and retraining pipeline established with Azure Machine Learning for adaptive improvements.
  • Significantly reduced manual email processing workload.
  • Faster routing of claims and inquiries.
  • Improved routing accuracy, reducing misclassified cases.
  • Enhanced responsiveness to customer emails.
  • Ability to monitor performance and improve model accuracy over time.
Architecture

Emails received in Dynamics CE using Dataverse trigger Power Automate. Azure AI Language Studio (NER) extracts the policy number; if found, a department lookup assigns the case. If not found, Azure AI Language Studio text classification is used. Power Automate then routes the case to the correct team with automatic updates in Dynamics. Monitoring is done with Azure Machine Learning.

Sources & evidence1
Groundedness: Unavailable

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