In-house Financial Services global Company newsroom

Munich Re

Munich Re needed a practical way to automate insurance product implementation and underwriting-related product design for insurers whose processes were still manual, disconnected, and constrained by unstructured data, legacy systems, and limited technical budgets.

a global team of more than 30 professionals
efficiency gains of up to US$ 160 billion by 2027
successfully implemented at 50 customers worldwide
the product design can be completed in a few hours or days

Reported impact

Business outcome

Munich Re reports that the CoPilot improves quality, accuracy, and consistency in product design, reduces time and resources needed for implementation, increases usability through natural-language interaction, and helps insurers speed time to market while lowering cost and effort.

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

Solution

Munich Re implemented REALYTIX ZERO CoPilot as a generative AI assistive function inside its cloud-based REALYTIX ZERO underwriting platform to automate insurance and reinsurance product building. Product designers and developers enter natural-language prompts and required parameters in a chat-style interface, and the CoPilot answers requests step by step during implementation. Its suggestions can be transferred directly into the REALYTIX ZERO product studio, where users configure or adapt products without needing special IT or coding skills. REALYTIX ZERO then serves as the operational environment for digital product configuration, policy and document setup, reporting, and API-based integration with other systems. The source describes prompt-driven guidance, conversational assistance, no-code/low-code implementation, and platform integration, but it does not disclose the underlying model provider, storage architecture, or internal data management components.

Data flow

Underwriters and product designers enter natural-language prompts and product parameters into REALYTIX ZERO CoPilot. The generative AI function interprets the request, generates implementation suggestions, and lets users transfer them directly into the REALYTIX ZERO product studio. Within the cloud-based platform, those configurations are used to build or adapt products, documents, policies, and notifications, with APIs connecting the setup to other systems.

Solution architecture

2 components · 1 layer
  1. Serving
    • REALYTIX ZERO Cloud-based underwriting platform where users digitally configure insurance products, documents, policies, reporting, and connected workflows.
    • REALYTIX ZERO CoPilot Generative AI chat-based assistive function that guides users through implementing or adapting insurance products and suggests logic that can be transferred into the product studio.

Architecture clues

  • Chat function similar to that of ChatGPT
  • Cloud-based underwriting platform
  • Extensive reporting tools for portfolio management
  • No-code/low-code product implementation and maintenance
  • Platform can be easily integrated with other systems through powerful APIs
  • Suggestions can be directly transferred into the REALYTIX ZERO product studio

Evidence from the source

"It provides suggestions which can be directly transferred into the REALYTIX ZERO product studio."
"REALYTIX ZERO and its CoPilot stands for easy integration, speeding up time to market, reducing costs and increasing efficiency."
"The CoPilot is a Gen AI-driven assistive function which supports designers and developers in implementing or adapting insurance products and smoothly guides them through every single step in the product implementation process."
"The CoPilot speeds up the process time impressively, so that the product design can be completed in a few hours or days."
"The platform is cloud-based, requires no/minimal programming skills and can be easily integrated with other systems through powerful APIs."
"The users do not have to bring any special IT or coding knowledge."
"With the new generative AI-driven CoPilot function integrated into the REALYTIX ZERO underwriting platform, Munich Re is at the forefront of underwriting automation development"
Transparency Evidence & methodology 75/100 reference value · 92% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    7 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. Munich Re

    Munich Re customer story

    First-party
    https://www.munichre.com/en/insights/digitalisation/generative-ai-munich-re-is-driving-automation-in-the-insurance-industry.html
    Source type
    Customer-published story
    Published
    May 22, 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 May 22, 2024 · Munich Re · 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.