Microsoft Financial Services Austria

Raiffeisen Bank International

Enterprise Knowledge Search · IT Operations Automation

RBI wanted to use AI to automate repetitive tasks like documenting intelligence and more rapidly summarizing legal, regulation, and banking documents.

RBI’s 43,000 employees offer a wide range of financial services, including retail and corporate banking, leasing, asset management, and mergers and acquisitions, serving 16.5 million customers in 11 markets.
The RBI ChatGPT was first deployed to 2,000 users and now has an active user base of over 20,000.

Reported impact

Business outcome

The RBI ChatGPT was first deployed to 2,000 users and now has an active user base of over 20,000. Employees report that previously time-consuming tasks can be performed quickly. They now have the flexibility to spend time on more value-added tasks or helping customers with complex questions.

  • Compliance
  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Raiffeisen Bank International built this solution on Azure OpenAI Service, Azure AI Search. RBI wanted to use AI to automate repetitive tasks like documenting intelligence and more rapidly summarizing legal, regulation, and banking documents. Azure OpenAI Service provides the generative AI model layer used for summarization, drafting, classification, or copilots in the workflow. Azure AI Search indexes enterprise content so assistants and applications can retrieve grounded answers. The company built the RBI ChatGPT, using Microsoft Azure OpenAI Service and Azure AI Search within Azure AI Foundry. With Azure AI Foundry , RBI used Azure OpenAI Service and Azure AI Search as primary tools. The rollout also connects with knowledge base so users can work inside existing operating processes. Business results include the RBI ChatGPT was first deployed to 2,000 users and now has an active user base of over 20,000.

Solution architecture

2 components · 2 layers
  1. Compute
    • Azure OpenAI Service Provides the generative AI model layer used for summarization, drafting, classification, or copilots in the workflow.
  2. Storage
    • Azure AI Search Indexes enterprise content so assistants and applications can retrieve grounded answers.

Architecture clues

  • The implementation relies on Azure OpenAI Service as a core technical building block.

Evidence from the source

RBI wanted to use AI to automate repetitive tasks like documenting intelligence and more rapidly summarizing legal, regulation, and banking documents.
RBI’s 43,000 employees offer a wide range of financial services, including retail and corporate banking, leasing, asset management, and mergers and acquisitions, serving 16.5 million customers in 11 markets.
The RBI ChatGPT was first deployed to 2,000 users and now has an active user base of over 20,000.
The RBI ChatGPT was first deployed to 2,000 users and now has an active user base of over 20,000. Employees report that previously time-consuming tasks can be performed quickly.
The company built the RBI ChatGPT, using Microsoft Azure OpenAI Service and Azure AI Search within Azure AI Foundry.
Transparency Evidence & methodology 81/100 reference value · 87% 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 value81/100
  • 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 specificity16/20
    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 confidence87%
  • 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 specificity8/10
    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

    Raiffeisen Bank International customer story

    First-party
    https://www.microsoft.com/en/customers/story/21406-raiffeisen-bank-international-azure
    Source type
    Vendor-published customer story
    Published
    Jan 30, 2025
    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 Jan 30, 2025 · 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.