Microsoft Financial Services Greece

National Bank of Greece

Document Processing

One area that the bank needed to automate to enhance its service was document processing. “Initially, a customer would bring the documents to a branch or email them, then our branch employee would print, scan, and then send them to the back office to manually extract the information," Georgia Zevgaropoulou elaborates.

So, it was an easy decision to continue with Azure.” “We were able to build the Document AI solution using Azure in less than four months.” Georgia Zevgaropoulou, Head of AI Sector in Artificial Intelligence and Technology Innovation Division, National Bank of Greece NBG started by analyzing 50 million pages of documents to create a dataset.
The bank serves 6 million customers and operates 356 branches across Greece.
This type of information can be crucial for our reporting, regulatory, or compliance matters.” With over 700,000 pages processed per month, this Azure-powered solution has become integral to NBG’s banking services.
We needed to process documents from more than 2 million customers in our digital channels in real time, and we needed to be accurate and consistent,” Georgia Zevgaropoulou adds.

Reported impact

Business outcome

This cutting-edge solution has improved the bank’s accuracy to 90%. By seamlessly integrating the solution with customer-facing systems, NBG has transformed its customer experience, enabled a fully digital journey, and drastically reduced waiting times.

  • Compliance
  • Customer Experience

Solution

National Bank of Greece used Azure AI Document Intelligence, Azure Cognitive Services, Microsoft Dynamics 365 to turn complex business content into faster digital workflows. One area that the bank needed to automate to enhance its service was document processing. “Initially, a customer would bring the documents to a branch or email them, then our branch employee would print, scan, and then send them to the back office to manually extract the information," Georgia Zevgaropoulou elaborates. Key implementation elements included Azure AI Document Intelligence extracts and structures information from business documents; Azure Cognitive Services adds speech, vision, or language capabilities required by the experience; Microsoft Dynamics 365 provides the common business process layer that standardizes operations; Microsoft Azure hosts the application, data, and integrations on scalable cloud infrastructure. Business documents and enterprise content are ingested into the AI workflow,...

Data flow

Business documents and enterprise content are ingested into the AI workflow, classified or summarized by the model layer, and routed into the application or operations process where employees can review and act on the output.

Solution architecture

4 components · 2 layers
  1. Compute
    • Azure AI Document Intelligence Extracts and structures information from business documents.
    • Azure Cognitive Services Adds speech, vision, or language capabilities required by the experience.
    • Microsoft Azure Hosts the application, data, and integrations on scalable cloud infrastructure.
  2. Orchestration
    • Microsoft Dynamics 365 Provides the common business process layer that standardizes operations.

Architecture clues

  • Operates with real-time or near-real-time telemetry and analytics.

Evidence from the source

One area that the bank needed to automate to enhance its service was document processing. “Initially, a customer would bring the documents to a branch or email them, then our branch employee would print, scan, and then send them to the back office to manually extract the information," Georgia Zevgaropoulou elaborates.
So, it was an easy decision to continue with Azure.” “We were able to build the Document AI solution using Azure in less than four months.” Georgia Zevgaropoulou, Head of AI Sector in Artificial Intelligence and Technology Innovation Division, National Bank of Greece NBG started by analyzing 50 million pages of documents to create a dataset.
The bank serves 6 million customers and operates 356 branches across Greece.
This cutting-edge solution has improved the bank’s accuracy to 90%. By seamlessly integrating the solution with customer-facing systems, NBG has transformed its customer experience, enabled a fully digital journey, and drastically reduced waiting times.
“When we tried Azure AI Document Intelligence—or Form Recognizer, as it was known then—for document processing, the results were impressive in terms of accuracy and the time to market.
Transparency Evidence & methodology 65/100 reference value · 90% confidence · 2 sources

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 value65/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence90%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 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 transparency9/10
    2 source references 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

    National Bank of Greece customer story

    First-party
    https://www.microsoft.com/en/customers/story/1774373123930343901-nbg-azure-banking-and-capital-markets-en-greece
    Source type
    Vendor-published customer story
    Published
    Jun 5, 2024
    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
  2. National Bank of Greece

    Group & Bank Annual Financial Report 2025

    First-party
    https://www.nbg.gr/-/jssmedia/Files/Group/enhmerwsh-ependutwn/Annual_Financial_Reports/Annual-Financial-Report-2025-EN.pdf

    The report states that the Group had "a workforce as at 31 December 2025 of 7,877 employees (Bank: 6,506)."

    Source type
    Official annual report
    Supports
    • Company size

Date evidence

  • Published on source page Jun 5, 2024 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.