Normalized claim
Cost: 51% decrease
51% cost reduction for automated processes.
Zurich Insurance Group Automates Claims and Policy Issuance Processes
Use case typeClaims automationUpdated Jun 13, 2026
Zurich Insurance Group, the largest insurer in Switzerland, sought to address inefficiencies in manual claims and policy issuance processes. The company collaborated with Ernst & Young to implement Robotic Process Automation (RPA) and AI-powered automation using Microsoft Azure and Blue Prism technologies. This transformation journey began with a proof-of-concept to pilot RPA in 2014, which was rolled out globally by 2015 across Zurich’s life insurance operations. Subsequently, the solution was extended to general insurance, enabling end-to-end automation of six key application flows per country. The implementation featured AI-driven document extraction and intelligent exception handling. As a result, Zurich achieved a significant reduction in operational costs, improved process speed and compliance, and freed up operational team resources. The project underlines the potential of enterprise-scale automation and human-robot collaboration for the insurance sector. Notably, Zurich set up a center of excellence for continuous automation delivery, sustaining ongoing improvements and rapid releases. Automated solutions now handle a significant portion of policy issuance, claims processing, and document management, with meaningful impact on both customer experience and internal efficiency.
- Organization
- Zurich Insurance Group
- Industry
- Insurance
- Location
- Switzerland
- Published
- September 2018
Reported outcomes
Cost: −20–30%
Cost savings
Time: Less than 50 daysTime: 4–5 hoursTime: 4–5 minutes
Catalog median for cost savings deployments: −40% across 171 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Quantified impact: 25%
25% of operational team capacity freed up.
Normalized claim
Time: 50 days decrease
Claim payment times reduced from 50 days (industry average) to under one week.
Normalized claim
Cost: 20-30% decrease
70% effort saved for straight-through processes, 20–30% for exceptions.
Normalized claim
Time: 4-5 hours decrease
Average manual handling per policy reduced from 4–5 hours to 40–80 minutes.
Normalized claim
Time: 4-5 minutes decrease
Average manual handling per policy reduced from 4–5 hours to 40–80 minutes.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Zurich Insurance Group
- Provider
- Microsoft
- Maturity
- Production
- Linked source
- altoros.com
This transformation journey began with a proof-of-concept to pilot RPA in 2014, which was rolled out globally by 2015 across Zurich’s life insurance operations
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 4
- 1Automated Claims Processing with AI and RPA
- 2End-to-End Policy Issuance Automation
- 3Intelligent Document Extraction for Insurance
- Implemented Robotic Process Automation (RPA) across core insurance processes using Microsoft Azure and Blue Prism platform.
- Integrated machine learning and OCR capabilities for processing unstructured data such as medical reports and certificates.
- Established a robotic center of excellence to deliver new automated workflows on a fast cycle.
- Designed bots to handle exception logic, reducing manual interventions and improving quality control.
- Enabled end-to-end automation for simple policies, with intelligent handoff for exceptions and human-robot collaboration.
- Claim payment times reduced from 50 days (industry average) to under one week.
- 70% effort saved for straight-through processes, 20–30% for exceptions.
- Average manual handling per policy reduced from 4–5 hours to 40–80 minutes.
Architecture
RPA bots are triggered by Zurich’s internal policy administration system to begin processing. The bots use Blue Prism to perform data validations, synchronize policy data across master and local systems, generate invoices, and draft policy documents using templates. Exception flows are managed by logic encoded within bots, which can auto-resolve, partially escalate, or fully escalate to human handlers. Machine learning OCR is layered on top for extracting data from unstructured sources such as medical reports and death certificates. The system frees humans for more complex tasks and iteratively delivers new automated processes via a dedicated center of excellence.
Sources & evidence3
Evidence: Medium60/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Quantified outcome available
- Technical implementation details available
- Multiple corroborating sources available
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.
Type: Blog PostPublished: Sep 17, 2018Publisher: altoros.comEvidence: VendorConfidence: Medium
AI-generated summary. Verify important details with the linked sources before relying on this case.
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