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Google Cloud Financial Services Switzerland

AXA Switzerland

Data Lake / Data Warehouse Modernization · Real-time Analytics · Risk Management

AXA Switzerland needed to move beyond fragmented legacy insurance systems, shorten slow analytics cycles, and build a cloud-native foundation that could support new AI-driven customer experiences, risk insights, and application modernization.

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Query and processing times reduced by over 95%, from days to minutes or seconds

Reported impact

Business outcome

AXA Switzerland reduced data-processing latency by more than 95%, created a stronger foundation for AI innovation, and translated that modernization into more personalized, future-focused insurance experiences.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

AXA Switzerland used Google Cloud to build a cloud-native analytics and AI foundation that could support both enterprise modernization and new customer-facing insurance services. BigQuery became the central data platform, bringing CRM, ERP, ecommerce, and operational information together in one place so teams could run business-critical queries in seconds or minutes instead of waiting through weekend batch cycles. On top of that data layer, AXA uses Vertex AI for machine-learning workflows and Cloud Run to deploy serverless applications that operationalize new models and digital services. The combination gives AXA a cleaner path from enterprise data migration to experimentation with natural-language interactions and more personalized protection workflows. Rather than treating AI as a side project, AXA anchored its broader digital-transformation program in a governed Google Cloud stack that ties data processing, model execution, and application delivery together.

Data flow

Enterprise data from CRM, ERP, ecommerce, and operations is centralized in BigQuery, where analytics and feature sets are prepared for downstream AI use. Vertex AI runs machine-learning workflows on that foundation, and Cloud Run hosts the serverless applications that surface new digital services and internal tools.

Solution architecture

3 components · 3 layers
  1. Compute
    • Vertex AI Supports machine-learning workflows that power AXA's next wave of AI-driven insurance products and services.
  2. Storage
    • BigQuery Unifies CRM, ERP, ecommerce, and operational data into a single analytics foundation for enterprise reporting and AI use cases.
  3. Serving
    • Cloud Run Deploys serverless applications that expose modernized data and AI capabilities to business users and customers.

Architecture clues

  • BigQuery centralizes CRM, ERP, ecommerce, and operational data
  • Cloud Run is used for serverless application deployment
  • The program is described as building a cloud-native analytics foundation
  • Vertex AI supports AXA's machine-learning workflows

Evidence from the source

Cloud Run for serverless application deployment
Query and processing times reduced by over 95%, from days to minutes or seconds
Vertex AI for machine learning workflows
What once took a weekend now takes seconds or minutes. It's a transformative difference.
With BigQuery, we were finally able to bring all our enterprise information together—CRM, ERP, ecommerce, and operational data—into one system.
Transparency Evidence & methodology 85/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Google Cloud

    AXA Switzerland customer story

    First-party
    https://cloud.google.com/customers/axa-switzerland-ai
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Mar 13, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Mar 3, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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