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GCC Insurance Provider ยท Insurance

Claims intake triage and document handling for an insurance provider

Automated claims intake classification, document completeness checking and handler assistance, with explicit human decision points.

21 April 2026 Insurance

Technologies

  • Dynamics 365 Customer Service
  • Copilot Studio
  • Power Automate
  • SharePoint Online
  • Azure AI Document Intelligence

Outcome themes

  • Faster intake
  • Reduced rework
  • Consistent documentation

Client context

A general insurance provider receives claims through email, a portal and a call centre. Handlers open each submission, identify the claim type, check whether the required documents are attached, and request anything missing. Only then does assessment begin.

The challenge

The pre-assessment work is substantial and repetitive, and incomplete submissions cause several rounds of correspondence before assessment can start. Regulatory obligations require that claim decisions remain with authorised handlers, so automation cannot extend to adjudication. Any solution must produce an audit trail suitable for regulatory review.

Objectives

  • Reduce manual classification and document-checking effort at intake
  • Identify incomplete submissions immediately rather than after a handler opens them
  • Keep every claim decision with an authorised human handler
  • Produce an audit trail suitable for regulatory review
  • Route sensitive circumstances to people by rule, not by model judgement

The solution

Submissions from all channels are normalised into a single intake queue. Document Intelligence extracts structured fields from common claim documents; a classification step assigns claim type and identifies missing mandatory documentation. Where a document is missing, an automated request is issued with the specific items required. A Copilot Studio agent supports handlers with case summarisation and policy reference lookup grounded in approved documentation. Adjudication remains entirely with authorised handlers, and every automated action is logged with inputs and confidence.

Architecture

  • Dynamics 365 Customer Service as the claims handling workspace
  • Azure AI Document Intelligence for structured extraction from claim documents
  • Power Automate for channel normalisation, completeness checks and correspondence
  • Copilot Studio agent for handler-side summarisation and policy lookup, grounded on approved content
  • SharePoint Online for claim documentation with retention labelling
  • Dataverse audit and custom action logging for regulatory evidence
  • Rule-based bypass routing sensitive circumstances directly to specialist handlers

Delivery approach

How the work ran

  1. Assess

    Twelve months of claim history analysed to establish intent distribution and document requirements by claim type.

    Volume analysis Claim type taxonomy Document matrix
  2. Design

    Intake flow, automation boundary, confidence thresholds, escalation rules and audit model.

    Automation boundary definition Audit design Escalation rules
  3. Build

    Extraction, classification, completeness checking, correspondence and handler assistance implemented incrementally.

    Configured intake pipeline Agent Evaluation set
  4. Validate

    Shadow running against live volume with handler review of every automated classification before activation.

    Accuracy evidence Threshold calibration Sign-off pack
  5. Operate

    Weekly review of accuracy, escalation and repeat contact, with threshold adjustment.

    Operating dashboard Review cadence Tuning log

Outcomes

What changed

Pre-assessment effort reduced

Classification and completeness checking happen before a handler opens the claim.

Fewer correspondence rounds

Missing documentation is requested at intake rather than after review.

Decisions remain with handlers

Adjudication is unaffected by automation, and this is evidenced in the audit trail.

Adjustable automation boundary

Thresholds are tuned weekly against observed accuracy rather than fixed at design time.

Lessons learned

What we would tell the next organisation

Every engagement produces something worth carrying forward. These are the points that mattered most in this one.

  • Shadow running before activation is the only reliable way to calibrate confidence thresholds.
  • Sensitive circumstances should bypass automation by explicit rule. Relying on model judgement for this is not defensible.
  • Handlers accept assistance readily when it is clear that decision authority remains theirs.

More customer stories

Next step

Start with the problem, not the platform

The first conversation is about what is not working today. Product selection comes later, and sometimes the answer is that you do not need a new one.

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