MicrosoftProductionEvidence: Medium65/100

Tokio Marine Japan and Central Insurance Automate Claims and Fraud Detection

Use case typeClaims automationUpdated Jun 13, 2026

Shift Technology deployed generative AI solutions, powered by Microsoft Azure OpenAI, to automate insurance document processing and fraud detection for global insurers. Claims professionals traditionally spent over 30% of their time manually extracting information from documents, causing delays and poor customer experiences. The implementation leverages Shift's proprietary insurance knowledge layer combined with Azure OpenAI Service, resulting in over 95% accuracy in document data extraction and more than 90% in subrogation liability assessment. Referral volume doubled and acceptance rates improved by 30%. Insurers like Tokio Marine Japan and Central Insurance now experience faster claims settlement and improved subrogation detection. The architecture automates extraction from structured and unstructured data, operates in the background, and delivers actionable insights without claims handlers needing AI prompt engineering expertise. Shift's solution is enterprise-grade with scalability, security, and reliability supported by Microsoft Azure. Shift's generative AI is proven in production use with documented impact for large insurers.

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Organization
Tokio Marine Japan
Industry
Insurance
Location
Japan
Published
June 2024

Reported outcomes

Impact: +30%

Other quantified impact

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Accuracy: 95%

PR NewswireJun 26, 2024News articleInferred claimMedium evidence strength

Achieved 95%+ accuracy in document information extraction.

Normalized claim

Accuracy: 90%

PR NewswireJun 26, 2024News articleInferred claimMedium evidence strength

Reached over 90% accuracy in subrogation liability determination.

Normalized claim

Quantified impact: 30% increase

PR NewswireJun 26, 2024News articleInferred claimMedium evidence strength

Doubled referral volume and increased acceptance rate by 30%.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
Customer
Tokio Marine Japan, Central Insurance
Provider
Microsoft
Maturity
Production
Linked source
PR Newswire

Shift Technology deployed generative AI solutions, powered by Microsoft Azure OpenAI, to automate insurance document processing and fraud detection for global insurers

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

  • 1Automated Document Information Extraction in Insurance Claims
  • 2AI-Powered Fraud and Subrogation Detection
  • 3Generative AI for Claims Automation
  • Implemented Shift Technology's AI-powered decision optimization solutions.
  • Integrated Microsoft Azure OpenAI Service with proprietary insurance knowledge.
  • Automated document extraction and subrogation liability assessment.
  • Delivered actionable insights to claims handlers and insurance professionals.
  • Seamless integration with insurer's systems for automated claims progress.
Technologies
  • Doubled referral volume and increased acceptance rate by 30%.
  • Reduced manual review workload, freeing claims professionals' time.
Architecture

Insurers use Shift Technology's generative AI, built on Microsoft Azure OpenAI Service and Shift's proprietary insurance knowledge layer, to process both structured and unstructured insurance documents. The solution operates as a data processing pipeline—extracting relevant data, transforming it into actionable insights, and integrating results into insurer claims systems automatically. The AI runs in the background, requiring no special prompt engineering from users. Data sources, including third-party integrations, are leveraged for enhanced fraud and subrogation detection. Solution is scalable, secure, and reliable by virtue of Azure cloud infrastructure.

Sources & evidence1
Evidence: Medium65/100Evidence strength
  • Customer explicitly identified
  • Deployment status explicitly supported
  • Independent source available
  • Quantified outcome available
  • Technical implementation details available
Type: News ArticlePublished: Jun 26, 2024Publisher: PR NewswireEvidence: SecondaryConfidence: Low
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