Shift Technology

Insurance fraud costs the property and casualty industry an estimated $308 billion annually in the United States alone, and fraudsters continuously adapt their tactics to evade detection. Shift Technology's fraud-detection platform ingested vast volumes of unstructured documents — claims forms, medical reports, police filings, invoices — arriving in multiple formats, languages, and layouts from hundreds of global insurers. Before each new document type could be processed, engineers spent weeks on data labeling, model training, and manual classification workflows. This bottleneck delayed fraud signals, slowed claims decisions, and limited the platform's ability to scale alongside client demand.

Shift Technology rebuilt its document intelligence pipeline on Microsoft Azure, integrating Azure OpenAI Service, Azure AI Vision, and Azure AI Document Intelligence into its core fraud-detection and claims-processing platform. Azure AI Vision handles machine learning-based OCR across heterogeneous document formats, while Azure AI Document Intelligence performs layout detection and structural parsing. On top of this extraction layer, Shift Technology applied prompt engineering with Azure OpenAI to convert raw, unstructured document content into structured, queryable data at scale — without requiring document-specific model retraining for each new format. Standardizing on a single cloud provider allowed the team to move from research to proof of concept to production rapidly, and Azure's regional deployment options satisfied strict data residency requirements for insurance clients across multiple jurisdictions.

Document classification and extraction workflows were compressed from weeks to days, dramatically accelerating the feedback loop between document ingestion and actionable fraud signals. The platform has now analyzed more than 2.6 billion policies, claims, and supporting documents across hundreds of global insurer clients. Qualitative outcomes include:

• Faster claims decisions delivered to insurers, reducing exposure windows for fraudulent payouts
• Reduced engineering overhead per document type, as prompt engineering libraries are reused across formats
• Continued access to frontier Azure OpenAI models without infrastructure rebuilds, keeping the platform current without additional spin-up costs
• Compliance with regional data security and residency regulations maintained throughout scale-up

• Single-cloud standardization pays compounding dividends: consolidating on Azure gave Shift Technology immediate access to new AI endpoints and eliminated cross-provider integration overhead.
• Prompt engineering libraries are reusable assets: extraction logic built for one document type can be adapted for others, reducing marginal onboarding costs per new insurer or document class.
• Hosted AI satisfies compliance requirements that self-hosted models complicate: Azure's regional deployments allowed Shift Technology to meet strict data residency mandates without sacrificing access to frontier generative AI.
• Separating OCR, layout detection, and LLM reasoning into distinct layers improves reliability and makes each component independently tunable as document volumes and types grow.