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Published 11 Jun 2025· via EY· Case study

Nordic Insurer Streamlines Claims Processing and Boosts Customer Service

Headline result

The source does not publish a single comparable figure. Reported results are listed below.

Summary

An unnamed Nordic insurer engaged EY to modernize manual claims processing across large volumes of varied, unstructured documents that existing technology could not process swiftly through end-to-end automation.

EY Fabric Document Intelligence, built on machine learning and Python and hosted in an EY-secured cloud, cleans scanned files and uses OCR and NLP to convert and classify unstructured data before transferring structured output to the core claims system.

The implemented solution provides near real-time claims document processing, with 70% of documents fed into the system correctly extracted and interpreted, giving agents more time for personalized customer interactions.

The problem

  1. 01Manual claim processing required agents to perform repetitive, time-consuming review of each request.
  2. 02Claims arrived as varied unstructured bills, invoices, pharmacy and clinic receipts, diagnoses, and supporting documents that required detailed examination and categorization.
  3. 03Existing technology could not support swift end-to-end automation, reducing customer service efficiency.

What they built

  1. 01EY analyzed the insurer's technology infrastructure and business needs and tailored EY Fabric Document Intelligence to its requirements.
  2. 02Built on machine learning and Python and hosted in an EY-secured cloud, the product cleans scanned images and performs preprocessing, document analysis, and layout analysis.
  3. 03Optical character recognition (OCR) and natural language processing (NLP) convert and classify unstructured data before structured output is transferred to the core claims system.
  4. 04Controlled confidence levels give the insurer transparency and control over the automation process.

Reported results

  1. 0170% of documents fed into the system are correctly extracted and interpreted.
  2. 02Claim documents are processed in near real time.
  3. 03Agents have more time for personalized advice and customer interactions.
  4. 04Operational efficiency and customer service improvements support the insurer's global expansion goals.
  • Near real-time claims processing
  • More time for personalized customer service
  • Supported global expansion goals
  • Automated claims document extraction

Source

EY

Case study · 11 Jun 2025

Read the original(opens in a new tab)

How this deployment compares

Computed from the library · data 10 Nov 2026

This record is one of 105 claims automation deployments, 92 of them in Insurance. Like 63% of records, its source gives no figure.

Same pattern, whole library

One of 105 claims automation deployments

  • Insurance accounts for 92 of them (88%), more than any other industry.
  • 78 records of this pattern came before it; 14 have been published since.
  • Only 24 of the 105 put a number on the result (23%).
Provider mix
  • Microsoft74%
  • AWS17%
  • GCP10%
Most-used technology
  • Azure OpenAI 34
  • Azure AI 25
  • Azure 15
  • Copilot 9
  • Azure AI Document Intelligence 8
  • AWS Lambda 8

Shares describe this library, not the whole market.

Claims automation analysis

Results against peers

No figure here. What peers report

Like 63% of records in the library, the source gives no figure. Peers that do quantify similar claims report:

  • Time & speed29 figures, Insurance · middle half 30%–80%40%median reduction
  • Customer experience50 figures, all industries · middle half 20%–90%28%median increase
  • Adoption & scale8 figures, all industries · middle half 134%–1100%250%median increase

Peer figures are as reported by their sources; percentage claims only.

How we checked this record

Evidence tier: supported

A credible source with a named customer, but not the organization's own account.

Named organization
No customer is named
Who published the source
The organization itself
Deployment status
Production (confidence 95%)
Quantified result
The source reports no figure
Summary checked against source
5 / 5 agreement
Source link
Online at last check

Last re-checked 13 Jun 2026.

This record is evidence for

Insight · industryInsurance dossierOne of 244 Insurance deployments. See which patterns dominate there and what they report.Insight · patternClaims automation105 deployments share this pattern. See where it is used and how it is built.Trend · momentumBuilding pattern8 new deployments in the last six months, against 3 in the six before.

Same pattern, other organizations

All claims automation deployments
  1. 4 Aug 2026
    Insurance

    Simpego automates Swiss insurance-claim document extraction with Amazon Bedrock

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    Simpego uses Anthropic Claude Opus on Amazon Bedrock in a serverless, event-driven pipeline to extract structured claim-document data and prefill its claims platform for specialist review. The pipeline handles variable-format multilingual Swiss documents, validates output schemas and invoice arithmetic, and keeps claims specialists as final decision-makers.

  2. 15 Jul 2026
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    Visma automates travel and expense claim validation with Amazon Bedrock

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    Visma, through M2 by Visma, built M2 Tarkka to automate validation of travel and expense claims on AWS. The solution combines deterministic rules with LLM-powered checks to interpret contextual free-text descriptions and apply Finnish tax and policy rules. Visma also added an AI agent so customers can upload travel policies and ask natural-language questions with source-attributed answers.

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  3. 4 Jul 2026
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    Gallagher: AI as a force multiplier for trusted advisors (claims summarization + quote automation)

    Gallagher · Claims automation · Microsoft

    Gallagher, a global insurance broker and risk management firm, built an AI platform on Microsoft Foundry to support secure enterprise AI in a highly regulated environment. The company deployed Microsoft 365 Copilot across 70,000 employees and used Copilot Studio to create governed agents connected to internal systems. AI is applied to claims summarization, document-heavy workflows, multi-line quote letter automation, and RFP automation with human oversight and strict data boundaries.

    AgenticProduction

  4. 9 Jun 2026
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    Hands-free FNOL intake with Strands Agents and Amazon Bedrock AgentCore Browser Tool

    Strands · Claims automation · AWS

    This article describes a hands-free first notice of loss (FNOL) intake prototype for insurance claims. It combines Strands Agents for insurance-specific reasoning with Amazon Bedrock AgentCore Browser Tool and Amazon Nova Act to operate existing portals, tag multimodal evidence, and produce decision-ready intake for adjusters.

    AgenticProduction