An AI Control Plane for Insurance Claims
A technical account of document extraction, claim summarization, intelligent routing, fraud analysis, and controlled system writeback.
A Claims Intelligence Control Plane
The central design decision was to place an intelligence layer between incoming claim documents and the brokerage's core systems. Those systems supported claims management, accounting, and regulatory reporting.
Azure Document Intelligence handled document extraction. Azure OpenAI large language model services supported claim summarization. Azure ML supported fraud and complexity models.
Python and FastAPI provided the application and service layer. Salesforce Service Cloud, Snowflake, and Power BI completed the operational and analytical architecture.
- Structured outputs were written to the downstream claims, accounting, and reporting systems.
- The control plane separated model processing from transaction systems.
- Human review remained part of decisions that required judgment.
Inputs Without a Common Shape
Claims arrived with medical records, police reports, repair estimates, invoices, correspondence, and contractor estimates. Adjusters reviewed these materials manually.
The source documents did not share a stable structure. Their data was then re-keyed into separate operational systems.
The existing fraud process combined a legacy rules engine with random audits. Regulatory reporting also depended on manual aggregation across systems.
- Property, casualty, and specialty claims used different supporting evidence.
- Document extraction needed to preserve source context for adjuster review.
- Fraud analysis needed to identify coordinated behavior and provider anomalies.
Extraction, Routing, and System Writeback
A multimodal OCR and NLP pipeline converted claim documents into structured fields. The pipeline then populated the connected downstream systems.
An LLM summarization service produced an executive brief for each claim. The brief highlighted key facts and coverage applicability. It also provided recommended next steps.
A complexity model routed routine claims toward straight-through processing. Complex claims were escalated with pre-populated analysis for senior adjusters.
- The extraction pipeline covered the document categories named in the source material.
- Complexity scoring controlled the route taken by each claim.
- System writeback reduced duplicate handling between claims and reporting workflows.
Human Review at the Decision Boundary
The design did not treat every model response as a final decision. Human-in-the-loop workflows placed adjusters at the boundary for high-value and complex claims.
The reviewer received extracted data and pre-populated analysis. Source documents remained available for validation.
This pattern kept judgment with claims professionals. Automation handled document interpretation, routing, and preparation for review.
Model Feedback and Regulatory Reporting
The fraud model used historical claims data to identify suspicious patterns. It also examined coordinated schemes and anomalous provider billing.
Adjuster feedback entered a continuous model improvement pipeline. New claim patterns could therefore be incorporated into retraining work.
Automated regulatory reporting aggregated data from the connected systems. Compliance validation was included before reporting output.
- Provider network analysis supported investigation of billing irregularities.
- Power BI exposed model and claims information for analysis.
- Snowflake supported the shared analytical data layer.
Client names and identifying details are withheld under confidentiality obligations.
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