Normalized claim
Quantified impact: 75% decrease
Reduced fraud-related losses for insurers by up to 75%
Last evidence check: Jul 22, 2026
Shift Technology revolutionizes insurance fraud detection
Use case typeClaims automationUpdated Jun 13, 2026
Shift Technology has implemented Azure OpenAI models to redefine the detection of claims fraud for insurers. Their solution improves operational efficiency, reduces fraud-related losses, and enhances user experience across the insurance lifecycle.
AI-generated summary. Generated from indexed evidence or computed site data. Underlying records pass applicable deterministic checks; verify important details with source links where available. How content is created.
- Organization
- Shift Technology
- Industry
- Insurance
- Location
- France
- Published
- April 2025
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Accuracy: 30% increase
Increased detection accuracy of fraudulent claims by over 30%
Last evidence check: Jul 22, 2026
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Shift Technology
- Provider
- Microsoft
- Maturity
- Production
- Linked source
- Microsoft Customer Stories
Their solution improves operational efficiency, reduces fraud-related losses, and enhances user experience across the insurance lifecycle
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 5
- 1AI-Powered Claims Fraud Detection
- 2Automated Claims Triage and Assessment
- 3Real-Time Risk Scoring for Submitted Claims
- Implemented Azure OpenAI models for advanced fraud pattern recognition
- Automated claims analysis using AI-driven insights
- Integrated fraud detection across the entire insurance claims lifecycle
- Enabled insurers to rapidly deploy and scale fraud detection capabilities
Technologies
Sources & evidence2
Evidence: Medium60/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Multiple corroborating sources available
- Recent evidence check available
- Last evidence check: Jul 22, 2026.
Live sourceStill referenced
The case's original source is still reachable.
- Cited source last checked Jun 12, 2026 — ok (0/2 broken).
Measures whether this deployment's public evidence persists — not whether the system is still in production.
Type: Customer StoryPublished: Apr 26, 2025Publisher: Microsoft Customer StoriesEvidence: PrimaryConfidence: High
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