Microsoft Financial Services Poland

Nest Bank

Enterprise Knowledge Search · IT Operations Automation

Building on its strategy of financing services for micro-entrepreneurs, Nest Bank decided to pursue advancements in automation. We started to automate almost every previously manual process at Nest Bank. If a process couldn’t be automated, we considered removing it altogether," Mitraszewska states.

In the last 12 months, we have doubled our sales.
Most of the time, our people work using Microsoft 365 applications—creating presentations in PowerPoint, documents in Word, or having meetings on Microsoft Teams.
There were times when failures would discourage our employees.
We’ve also increased the transactions made through our mobile application, from an average of 60,000 to 80,000 daily.

Reported impact

Business outcome

Nest Bank’s strategy to cultivate both efficiency and innovation has paid off. We encourage people to be creative and brave, which is not easy," shares Mitraszewska. There were times when failures would discourage our employees.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Nest Bank built this solution on Azure OpenAI Service, Azure Databricks, Microsoft 365 Copilot, Microsoft Teams. Building on its strategy of financing services for micro-entrepreneurs, Nest Bank decided to pursue advancements in automation. We started to automate almost every previously manual process at Nest Bank. Azure OpenAI Service provides the generative AI model layer used for summarization, drafting, classification, or copilots in the workflow. Azure Databricks processes, transforms, and engineers large data sets for analytics and machine learning. Microsoft 365 Copilot assists employees with drafting, summarization, and information retrieval inside daily work tools. Microsoft Teams presents collaboration workflows and shared communication experiences to end users. Nest Bank started by leveraging Azure OpenAI Service to build an internal solution—an AI assistant that can automate and simplify a range of administrative tasks.

Data flow

Operational data is collected from line-of-business systems, engineered and transformed in Azure Databricks before Microsoft Teams shares the output with end users.

Solution architecture

4 components · 2 layers
  1. Compute
    • Azure OpenAI Service Provides the generative AI model layer used for summarization, drafting, classification, or copilots in the workflow.
    • Azure Databricks Processes, transforms, and engineers large data sets for analytics and machine learning.
  2. Serving
    • Microsoft 365 Copilot Assists employees with drafting, summarization, and information retrieval inside daily work tools.
    • Microsoft Teams Presents collaboration workflows and shared communication experiences to end users.

Architecture clues

  • The solution is built around a governed cloud data foundation for shared analytics.

Evidence from the source

Building on its strategy of financing services for micro-entrepreneurs, Nest Bank decided to pursue advancements in automation. "We started to automate almost every previously manual process at Nest Bank.
Building on its strategy of financing services for micro-entrepreneurs, Nest Bank decided to pursue advancements in automation. We started to automate almost every previously manual process at Nest Bank.
Most of the time, our people work using Microsoft 365 applications—creating presentations in PowerPoint, documents in Word, or having meetings on Microsoft Teams.
Nest Bank’s strategy to cultivate both efficiency and innovation has paid off. We encourage people to be creative and brave, which is not easy," shares Mitraszewska.
There were times when failures would discourage our employees.
Transparency Evidence & methodology 81/100 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 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 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

    Nest Bank customer story

    First-party
    https://www.microsoft.com/en/customers/story/1782799284354734628-nestbank-azure-databricks-ai-banking-and-capital-markets-en-poland
    Source type
    Vendor-published customer story
    Published
    Jun 25, 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

Date evidence

  • Published on source page Jun 25, 2024 · 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.