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Microsoft Financial Services Japan

Hokkoku Bank

Customer Service Automation · Data Lake / Data Warehouse Modernization · Real-time Analytics

Hokkoku Bank needed to modernize and keep pace with technological advances, but as a regional bank, its resources were limited.

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Hokkoku Bank reimagines banking innovation, saves 10 percent on costs with Azure and the Microsoft Cloud
With the optimization of Hokkoku Bank’s digital estate, costs have decreased, and capabilities have increased since the rollout began. And the bank’s time to market has dramatically decreased by using Azure. “With our old systems, a new deployment took two to three months to get up and running,” shares Shintani. “Now, we just set up in the Azure portal and it’s usable in five minutes. We use only the Azure capacity needed, and we don’t have to match our peak usage. This is leading to cost reductions of approximately 10 percent, and we expect that to grow as we make the system more cloud native.”
can be from 22 to 93 percent more energy efficient than traditional enterprise datacenters. “We share these figures, and it’s very helpful,” he says. “It’s an important reason why we want to move our entire estate to the Azure platform.”

Reported impact

Business outcome

Hokkoku Bank reimagines banking innovation, saves 10 percent on costs with Azure and the Microsoft Cloud

  • Compliance
  • Cost Reduction
  • Productivity Improvement
  • Revenue Growth

Solution

Hokkoku Bank, a Financial Services organization in Japan, modernized this customer service automation workload with Windows Server, GitHub, Microsoft Teams, Power BI, and Azure Machine Learning. Hokkoku Bank needed to modernize and keep pace with technological advances, but as a regional bank, its resources were limited. Windows Server hosts legacy or supporting workloads that remain part of the hybrid application estate. GitHub supports code collaboration and developer workflow automation around the solution. Microsoft Teams acts as the collaboration surface where users consume insights, copilots, and workflow updates. Data from operational systems is ingested into Windows Server, processed across GitHub, and surfaced through Microsoft Teams so teams can act on fresher operational insight. Hokkoku Bank reimagines banking innovation, saves 10 percent on costs with Azure and the Microsoft Cloud.

Data flow

Data from operational systems is ingested into Windows Server, processed across GitHub, and surfaced through Microsoft Teams so teams can act on fresher operational insight.

Solution architecture

5 components · 3 layers
  1. Compute
    • Windows Server hosts legacy or supporting workloads that remain part of the hybrid application estate.
    • Azure Machine Learning trains, manages, and deploys machine learning models used in the production workflow.
  2. Serving
    • Microsoft Teams acts as the collaboration surface where users consume insights, copilots, and workflow updates.
    • Power BI publishes dashboards and self-service analytics so business users can act on fresher insights.
  3. Orchestration
    • GitHub supports code collaboration and developer workflow automation around the solution.

Evidence from the source

GitHub and Azure
Hokkoku Bank needed to modernize and keep pace with technological advances, but as a regional bank, its resources were limited.
Hokkoku Bank needed to modernize and keep pace with technological advances, but as a regional bank, its resources were limited. The bank laid the initial groundwork for modernization by moving its entire on-premises estate to Windows Server. This created a seamless path to full cloud transformation on Microsoft Azure. Currently, most of the bank’s operations run on Azure, and it plans to move to a fully cloud-based estate. By adopting a wide range of Azure solutions, Hokkoku Bank has been able to stay competitive, reduce infrastructure costs, and streamline processes for greater efficiency.
Hokkoku Bank reimagines banking innovation, saves 10 percent on costs with Azure and the Microsoft Cloud
Microsoft Teams
Transparency Evidence & methodology 90/100 reference value · 89% 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 value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    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 impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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 provenance10/10
    The publication date comes from the source page or publisher.
  • 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. Microsoft

    Hokkoku Bank customer story

    First-party
    https://www.microsoft.com/en/customers/story/1687922579120746345-hokkoku-bank-capital-markets-azure-en-japan
    Source type
    Vendor-published customer story
    Published
    Oct 2, 2023
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Oct 2, 2023 · Microsoft · First-party
    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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