Normalized claim
Time: 80% decrease
Reduce migration time by 80%.
Mainframe to Guidewire ClaimCenter Migration Agent
Use case typeClaims automationUpdated Jun 13, 2026
Insurance carriers migrating legacy mainframe claims data to Guidewire ClaimCenter face scarce expertise bottlenecks and complex attribute mappings across ~1,000 mainframe attributes to 40,000+ Guidewire attributes. The solution uses client-specific mainframe custom schemas and Guidewire ClaimCenter v9/v10 data dictionaries securely hosted in Amazon S3 and indexed with Amazon OpenSearch Serverless. Amazon Bedrock-based agents drive multi-phase workflows to recommend one-to-one, one-to-many, and many-to-one mappings, automate COBOL-to-configuration transformations, and perform end-to-end validation with lineage and sample comparisons.
- Organization
- Insurance carriers
- Industry
- Insurance
- Location
- United States
- Published
- June 2026
Reported outcomes
−80%
timeTime & speed
−95%accuracy
Strategic outcomes
New product / capabilityBuilt AI migration agent for claims mappingNew product / capabilityAutomated data conversion and validationRisk & complianceImproved data integrity and complianceCost efficiencyReduced dependence on scarce experts
Catalog median for time & speed deployments: −50% across 312 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Accuracy: 95% decrease
Reduce mapping errors by up to 95%.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Insurance carriers
- Provider
- AWS
- Maturity
- Unknown
- Linked source
- AWS Marketplace
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 4
- 1Migration Automation
- 2Claims Processing
- 3Data Mapping
- Insurance carriers migrating legacy mainframe claims data to Guidewire ClaimCenter face scarce expertise bottlenecks and complex attribute mappings with high mapping error risk.
- Manual mapping depends on scarce professionals who understand both mainframe structures and Guidewire data models.
- Built an AI migration agent that uses vector search over client-specific schemas and Guidewire data dictionaries to recommend optimal mappings.
- Automates data type and business logic conversions, including COBOL-to-Guidewire configuration transformations.
- Performs end-to-end validation, reconciliation reporting, lineage tracing, rollback support, and parallel runs within a guided GenAI workflow.
- Reduce migration time by 80%.
- Reduce mapping errors by up to 95%.
- Save millions in consulting fees by removing dependence on scarce mainframe and Guidewire experts.
- Improve data integrity and compliance through audit trails and reconciliation reports.
Architecture
The solution uses Amazon Bedrock-based agents with guardrails, Amazon OpenSearch Serverless as a vector database, and Amazon S3 to securely host client mainframe schemas and Guidewire ClaimCenter v9/v10 data dictionaries. It supports semantic retrieval, automated mapping recommendations, transformation logic, and validation/reconciliation workflows.
Sources & evidence1
Evidence: Low35/100Evidence strength
- Customer explicitly identified
- Quantified outcome available
- Technical implementation details available
Published: Jun 1, 2026Publisher: AWSEvidence: VendorConfidence: Medium
AI-generated summary. Verify important details with the linked sources before relying on this case.
Explore related AI use cases
Customer evidence
Provider evidence
Browse the catalog
Was this useful?
Community
Comments
No published comments yet.