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Microsoft Financial Services Switzerland

UBS

Enterprise Knowledge Search · Risk Management

UBS faced challenges in delivering personalized insights to clients due to complex workflows and content systems, hindering efficiency in providing comprehensive financial services solutions and timely, tailored advice to its clientele.

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Within 10 months, Azure OpenAI Service reached the key divisions Personal & Corporate Banking, Wealth Management Switzerland, Global Wealth Management, and Group Operations and Technology Office within UBS in the Booking Centers Switzerland, Hong Kong, and Singapore, and has already been rolled out to 30,000 employees as of December 2024.

Reported impact

Business outcome

With Azure AI and cloud services, it enhanced operational efficiency and data insights, leading AI-driven innovation across the financial services industry.

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

Solution

UBS built its Microsoft solution to improve knowledge retrieval and expert-assistance workflows. UBS faced challenges in delivering personalized insights to clients due to complex workflows and content systems, hindering efficiency in providing comprehensive financial services solutions and timely, tailored advice to its clientele. Key components included Azure OpenAI Service ran model inference or heavy processing, and Azure AI Search delivered dashboards, apps, or copilots to users. User prompts and domain content are orchestrated through Azure OpenAI Service, processed by Azure OpenAI Service, and then delivered through Azure OpenAI Service so UBS can return context-aware answers or automation in the flow of work. The stack stayed governed and extensible on Microsoft. With Azure AI and cloud services, it enhanced operational efficiency and data insights, leading AI-driven innovation across the financial services industry.

Data flow

User prompts and domain content are orchestrated through Azure OpenAI Service, processed by Azure OpenAI Service, and then delivered through Azure OpenAI Service so UBS can return context-aware answers or automation in the flow of work.

Solution architecture

2 components · 2 layers
  1. Compute
    • Azure OpenAI Service Runs large language models that generate grounded responses, summaries, and copilots.
  2. Serving
    • Azure AI Search Indexes enterprise content so copilots and applications can retrieve grounded answers.

Architecture clues

  • Claire Corish, Head of Analytics and Sales Platforms of Group Operations and Technology Office, UBS Looking toward the future, Jonas Isliker, Head of AI, Data & Analytics of Global Wealth Management at UBS, says, "Our biggest priority is rapidly moving into AI transformation.
  • Moreover, Microsoft's investments in building secure, local datacenters in Switzerland, and its willingness to strengthen the security policies for us, were fundamental enablers." The collaboration between UBS and Microsoft was driven by our need for flexibility and a robust technological roadmap.
  • This approach enabled UBS to develop contextual interactions that significantly enhanced its service offerings, fully capitalizing on the flexibility and customization capabilities of Azure.
  • This modernization has been underpinned by advanced security, management, and operational features, ensuring the bank maintains its commitment to data protection and regulatory compliance.

Evidence from the source

Developed using Azure AI Search and Azure OpenAI Service , these AI-powered tools provide support to Client Advisors during customer interactions.
These elements are essential in addition to meeting the growing demand for AI use cases across the organization." Azure AI Search was implemented to apply sophisticated search services, supporting advanced retrieval strategies and uncovering new insights from unstructured data using language model reasoning.
UBS faced challenges in delivering personalized insights to clients due to complex workflows and content systems, hindering efficiency in providing comprehensive financial services solutions and timely, tailored advice to its clientele.
UBS selected Microsoft Azure AI solutions, including Azure AI Search and Azure OpenAI Service, to power "Smart Assistants" that streamline content access and provide real-time information to Client Advisors, boosting efficiency and client engagement.
With Azure AI and cloud services, it enhanced operational efficiency and data insights, leading AI-driven innovation across the financial services industry.
Within 10 months, Azure OpenAI Service reached the key divisions Personal & Corporate Banking, Wealth Management Switzerland, Global Wealth Management, and Group Operations and Technology Office within UBS in the Booking Centers Switzerland, Hong Kong, and Singapore, and has already been rolled out to 30,000 employees as of December 2024.
Transparency Evidence & methodology 61/100 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 value61/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    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
    6 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

    UBS customer story

    First-party
    https://www.microsoft.com/en/customers/story/19796-ubs-azure
    Source type
    Vendor-published customer story
    Published
    Jan 27, 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 27, 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.

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