What challenges did Shift Technology face before AI implementation?

Insurance fraud detection is complex, with continually evolving schemes that outpace legacy systems. Processing vast amounts of unstructured insurance documents required weeks of manual analysis. Ensuring compliance and data residency was difficult for clients in different regulatory regions. Scaling infrastructure for rapidly growing document processing and AI workloads was a technical challenge. Maintaining transparency and security for sensitive insurance data was critical.

Source

MicrosoftExpandedProductionEvidence: Medium55/100

Shift Technology accelerates fraud detection and claims processing for insurers

Use case typeClaims automationUpdated Jun 13, 2026

Shift Technology transformed insurance fraud detection and claims processing by standardizing its AI-powered decision optimization solutions on Microsoft Azure. The company addresses the ever-evolving landscape of insurance fraud by leveraging Azure OpenAI Service, Azure AI Vision, and Azure AI Document Intelligence for automated document classification and data extraction. The new platform enables rapid deployment of AI endpoints, significantly accelerating the time needed for analyzing complex insurance policies and claims. By hosting workloads in regional Azure data centers, Shift meets strict security and compliance requirements for global clients. The result is improved fraud detection accuracy, streamlined claims processing, and enhanced operational efficiency, helping insurers stay ahead of emerging fraud trends and regulatory demands. Shift’s continuous innovation includes testing new agentic systems and deploying developer productivity tools like GitHub Copilot.

Organization: Shift Technology
Industry: Insurance
Location: France
Published: March 2023

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Shift Technology
Provider: Microsoft
Maturity: Production
Linked source: shift-technology.com

The result is improved fraud detection accuracy, streamlined claims processing, and enhanced operational efficiency, helping insurers stay ahead of emerging fraud trends and regulatory demands

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Automated Document Classification and Data Extraction for Insurance Claims
2AI-powered Fraud Detection in Insurance
3Scalable Claims Processing Automation

Insurance fraud detection is complex, with continually evolving schemes that outpace legacy systems.
Processing vast amounts of unstructured insurance documents required weeks of manual analysis.
Ensuring compliance and data residency was difficult for clients in different regulatory regions.
Scaling infrastructure for rapidly growing document processing and AI workloads was a technical challenge.
Maintaining transparency and security for sensitive insurance data was critical.

Migrated AI and ML workloads to Microsoft Azure to leverage cloud scale and global reach.
Used Azure OpenAI Service and GPT models for generative AI, prompt engineering, and rapid model deployment.
Implemented Azure AI Vision and Document Intelligence for accurate OCR, layout detection, and document data extraction.
Standardized on Azure Kubernetes Service for scalable, containerized AI deployments.
Used Azure Service Bus, Container Apps, and Functions for modern orchestration and event-driven workflows.
Implemented secure and compliant regional hosting, ensuring data isolation for sensitive insurance information.

Technologies

Azure OpenAI Azure AI Vision Azure AI Document Intelligence Azure Kubernetes Service Azure Service Bus Azure Container Apps Azure Functions Azure

Empowered insurers to respond faster to evolving fraud trends.

Architecture

Document data is ingested and classified using Azure AI Vision OCR and Azure AI Document Intelligence on Azure. Extracted information is processed with Azure OpenAI models for fraud detection and claims optimization. Containerized workloads run on Azure Kubernetes Service for scalability, orchestrated using Azure Service Bus and Container Apps. Data and models are deployed in regional Azure data centers to ensure security and compliance.

Sources & evidence1

Evidence: Medium55/100Evidence strength