MicrosoftExpanded

Zurich Insurance Group automates claims with Azure ML and Explainable AI

Use case typeClaims automationUpdated Jun 13, 2026

Zurich Insurance Group in Germany significantly improved property claims settlement by adopting Azure ML to automate customer information extraction, risk analysis, and policy validation with 98% accuracy. The solution includes an MLOps platform and Explainable AI, streamlining the claims process from days to hours. Azure ML processes customer data and documents, automates extraction from car documents and forms, analyzes policy and risk data, and delivers explainable outputs to improve human understanding. The project, completed with support from Saxon AI, focuses on regulatory compliance and better decision-making. Benefits include much faster claims turnaround, lower operational costs, and improved customer experience.

Industry
Insurance
Location
Germany
Published
November 2021

Reported outcomes

98%

accuracyQuality & accuracy

Strategic outcomes

Speed & agilityClaims settled much fasterRisk & complianceStrengthened compliance and auditabilityCustomer experience & trustImproved customer satisfactionNew product / capabilityEnabled explainable AI claims handling

Catalog median for quality & accuracy deployments: +90% across 282 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 2 of 2

  • 1Automated Claims Processing with Azure ML
  • 2Document Extraction for Property Insurance
  • Long settlement times for property claims negatively impacted customer experience.
  • Manual extraction and processing of documents was inefficient and error-prone.
  • Need for more accurate and compliant risk analysis and policy review.
  • Difficulty scaling claims processing during peak demand.
  • Automated claims processing workflow using Azure ML.
  • Implemented MLOps for scalable deployment and monitoring.
  • Used Explainable AI to make model decisions interpretable for claim handlers.
  • Partnered with Saxon AI for technical and integration support.
Technologies
  • Reduced claims settlement from days to hours.
  • Achieved 98% accuracy in document extraction and policy validation.
  • Improved operational efficiency and customer satisfaction.
  • Strengthened compliance with automated, auditable AI.
Implementation partners1
Sources & evidence1
ExpandedExpanded

The same organization appears in newer AI deployment evidence.

  • Same organization re-documented as recently as 2025.

Measures whether this deployment's public evidence persists — not whether the system is still in production.

Groundedness: Unavailable

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