Cognizant Financial Services Norway

Storebrand Bank

Document Processing

Storebrand Bank needed to accelerate lending and other customer-facing banking processes while coping with rising operational volume, but its automation work was fragmented across silos and too dependent on manual effort.

10 minutes off every credit card application
24/7/365 operations
40% faster delivery of automation solutions
50 automations delivered in 18 months

Reported impact

Business outcome

Storebrand Bank shortened customer-value delivery times, scaled automation across back-office and front-office work, and created a 24/7 operating model that supports faster approvals and more consistent service.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

Cognizant and Storebrand Bank turned a siloed automation effort into a dedicated Automation-as-a-Service operating model for banking operations. A multi-skilled POD team prioritized the automation pipeline, then moved early attended automations to unattended workflows orchestrated through UiPath Automation Cloud so routine jobs could run continuously without manual triggering. For document-heavy steps, Cognizant integrated a third-party generative AI service that extracts unstructured information from incoming documents and passes structured values to bots for downstream processing. Manual approval checkpoints stay inside the workflow when a human decision is required, and KPI dashboards measure cycle-time and efficiency gains across the automation portfolio. The result is a scalable framework that combines document AI, RPA, human validation and SLA-backed support to accelerate credit-card approvals, loan establishment and other consumer-banking processes.

Data flow

Applications and supporting documents enter Storebrand Bank workflows, where a third-party generative AI service extracts key information from unstructured files and passes structured values into unattended UiPath-orchestrated bots. The workflows include human validation when approvals are required, while KPI dashboards capture cycle-time and savings data across the automation portfolio.

Solution architecture

5 components · 4 layers
  1. Compute
    • third-party generative AI solution Extracts unstructured information from relevant documents before handing the results to bots for processing.
  2. Serving
    • KPI dashboards Track automation impact and efficiency gains across the bank’s delivery pipeline.
  3. Orchestration
    • UiPath Automation Cloud Orchestrates the unattended automations that replaced Storebrand’s earlier attended bots.
    • unattended workflows Run time- and event-triggered banking processes at scale without manual initiation.
  4. Governance
    • Automation as a service (AaaS) Provides the SLA-backed delivery and support model for the bank’s automation estate.

Architecture clues

  • Generative AI is inserted into selected workflows only for extracting information from unstructured documents.
  • Manual approval stages remain in the automation chain where a human must validate the result.
  • Storebrand shifted from attended bots to unattended workflows orchestrated through UiPath Automation Cloud.
  • The bank moved from project delivery to an SLA-backed Automation-as-a-Service model.

Evidence from the source

“Bots have replaced manual processes across the bank’s back-office and customer-facing operations, saving 7,000 hours in the first 18 months.”
“For some workflows, we integrated a bot with a third-party generative AI solution that extracts unstructured information from relevant documents.”
“Median lead times for customer value delivery have been shortened to around 90 minutes for daily banking, and around 2.5 hours for loan establishment.”
“Once the generative AI has extracted the key information, it passes it to the bot for processing.”
“we advised moving to unattended workflows orchestrated via UiPath Automation Cloud.”
Transparency Evidence & methodology 95/100 reference value · 88% 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 value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    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
    6 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence88%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability23/25
    11 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 transparency10/10
    2 source references 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. Cognizant

    Storebrand Bank embraces automation to deliver instant customer value | Cognizant

    First-party
    https://www.cognizant.com/us/en/case-studies/storebrand-bank-embraces-automation-to-deliver-instant-customer-value
    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
  2. Brønnøysundregistrene

    STOREBRAND BANK ASA

    Government
    https://virksomhet.brreg.no/nb/oppslag/enheter/953299216

    The page embeds entity data with “antallAnsatte”:220.

    Source type
    Government or regulatory registry
    Supports
    • Company size

Date evidence

  • First archived Oct 5, 2025 · 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 Aug 19, 2025 · Cognizant · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.