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Property and casualty insurance runs on data. Yet without shared standards, every integration becomes a one-off, every mapping introduces risk, and every new requirement slows innovation. In a recent webinar hosted by Cloverleaf Analytics, leaders from AAIS, Cloverleaf Analytics, and Perr&Knight explored how openIDS (Open Insurance Data Standards) can change that by delivering an open, extensible, and technology-agnostic approach to data exchange across the P&C insurance ecosystem.

The webinar “How openIDS is transforming data exchange in P&C Insurance” featured moderator: Werner Kruck, President & CEO, AAIS and panelists: Ken Sayers, VP of Technology, AAIS; Darren Totten, Director of Data Solutions, Perr&Knight; and Robert Clark, Founder & CEO, Cloverleaf Analytics. Here’s what they discussed.

What is openIDS?

OpenIDS is a community-driven standard under openIDL at the Linux Foundation that defines what data the industry needs to exchange and how to represent it consistently. It does not lock anyone into a specific vendor or transmission protocol. Teams can move data in JSON, CSV, XML, or other formats while staying aligned on the schema, codes, and definitions.

“It’s open and free forever… managed and owned by the Linux Foundation. None of us owns the standard.” – Ken Sayers, AAIS

Why it matters

Panelists aligned on three pain points openIDS is designed to solve:

• Integration friction: Integrations and historical data conversions are often the “longest poles in the tent.” A shared model shortens timelines and reduces project risk.
• Quality and trust: Repeated remapping is like making a photocopy of a photocopy. A standard preserves fidelity and improves downstream analytics.
• Speed to value: Implement the standard once, then reuse for carriers, MGAs, reinsurers, bureaus, vendors, and regulators.

The bottom line is the less time we spend untangling formats, means more time we can spend on underwriting, pricing, analyzing risk, serving customers, and complying with data calls.

How openIDS was developed

• Proven foundation: Cloverleaf contributed a mature, production-tested core model plus extensions across 47+ lines of business.
  
• Pragmatic design: The model flattens policy, coverage, risk, and claim details where appropriate to make data transfer and integrity simpler than highly normalized operational schemas.
  
• Extensible by design: The standard supports line-of-business and organization-specific extensions without breaking compatibility, so the community can evolve quickly as new data emerges (think EV attributes, IoT signals, new perils).

Where things stand

• Homeowners v1.0: The first openIDS line is out for comment and targeted for release as version 1.0.
• Parallel workstreams: Additional lines of business are being queued based on community demand and impact.
• Near-term demonstrations: Expect proofs of concept showing end-to-end reporting and dashboards built on the standard to follow early releases.
  

“As policy admin and claims vendors, regulatory bodies, and service providers adopt openIDS, ‘integration’ stops being a dreaded project and becomes plug-and-play.” – Robert Clark, Cloverleaf Analytics

Practical use cases

For insurers and managing general agents (MGAs)

• Faster statistical reporting and data calls
• Cleaner pipelines for pricing, reserving, and portfolio analytics
  
• Easier data sharing with fronting partners and reinsurers
  

For reinsurers and brokers

• Standardized bordereaux and exposure submissions
• Reduced bespoke mappings across counterparties
  

For vendors and service providers

• Reusable connectors and accelerators
• Less time on Extract, Transform, Load (ETL), more time on analytics, product, and user experience (UX)

For regulators

• Consistent, higher-fidelity data with lower burden on filers
• Ability to ask timely questions without imposing costly custom formats

“Integrations always succeed when both sides agree on a common standard. The hardest work isn’t technology, it’s alignment.” – Darren Totten, Perr Knight

How openIDS compares to proprietary formats

• Open and free: No license fees, no vendor lock-in.
• Tech-agnostic: Use JSON, CSV, XML, or other transports that fit your stack.
• Evolves with the market: Extensions let you add new attributes immediately while the community standardizes them over time.

AI makes standards more valuable

As organizations adopt AI for analytics, fraud detection, and automation, data quality is everything. Standardized, well-defined, and consistent inputs make AI outputs more reliable and explainable. Panelists also noted emerging opportunities to apply AI to accelerate mapping from legacy systems into the openIDS schema.

Key takeaways

• Reduce cost and complexity: Shrink timelines for integrations, conversions, and reporting.
• Improve accuracy: Preserve data fidelity and avoid drift from repeated remaps.
• Move faster together: One shared language unlocks innovation across the value chain.
• Have a voice: Members help set priorities, choose next lines of business, and shape the roadmap.

Call to action

Ready to help modernize P&C data exchange? openIDS grows with participation. There are simple ways to start:

• Become a member of openIDL. Membership is open to carriers, MGAs, reinsurers, brokers, regulators, vendors, and partners. Fees scale by company size.
• Contribute to the standard. The openIDS Working Group and Homeowners Workstream meets on Mondays. Join an openIDS working group or workstream
• Share feedback on the Homeowners openIDS Residential Structure Model.
• Share assets: Review specs, contribute schemas and sample data, validate implementations, or pilot an integration.
• Promote adoption: Socialize the standard with partners and vendors to grow the network effect.

To watch the webinar on demand, click here, and request permission to view.

Together, we can replace one-off integrations with a durable, open standard that accelerates innovation and strengthens the entire insurance ecosystem.

We’re pleased to make available for review the first openIDS data standard, built around an insurable object (a residence) and aligned with core insurance records. The Residential Structure Model defines a large range of attributes that help insurers evaluate residential risks. 

The openIDS Residential Structure Model includes code lists, risk indicators, and coverage indicators. 

We’re pleased to make available for review the first openIDS data standard, built around an insurable object (a residence) and aligned with core insurance records. The Residential Structure Model defines a large range of attributes that help insurers evaluate residential risks. 

Residential Structure Model

In insurance, an “insurable object” refers to any item that may be covered or excluded by a policy. By standardizing how residential structures are described and recorded, we’re enabling:

• Improved risk assessment – Consistent data means more accurate underwriting.
• Regulatory readiness – Quicker responses to regulators with data in recognized formats.
• Seamless interoperability – Shared definitions reduce the need for costly one-off data translations.
• Better insights – Rich, structured data unlocks more powerful analytics and product innovation.

How to Give Feedback

This release is a milestone for open insurance data standards. We invite you to review the Residential Structure Model and provide feedback. There are two easy ways to share your feedback:

1. Add comments directly in the draft of the model
2. Email your suggestions to the OpenIDL Executive Director, Josh Hershman

Comments are due by September 25.

Review now.

Your feedback now will shape the future of insurance data standards.

The insurance industry has long struggled with fragmented data — every carrier, regulator, and service provider using its own formats, slowing down compliance, innovation, and collaboration. That’s where openIDL and openIDS come in.

openIDL

openIDL (Open Insurance Data Link) is the umbrella initiative under the Linux Foundation. It was launched in 2018 to bring insurers, regulators, and technology providers together around a common mission: make insurance data exchange faster, more secure, and more useful for everyone. Early on, openIDL proved blockchain could handle sensitive insurance data through a successful pilot in North Dakota, where carriers, the DMV, and regulators exchanged data on uninsured motorists.

But the industry asked for more. Carriers and regulators didn’t just want a faster pipe for data — they wanted a shared language. That’s why openIDL pivoted to focus on building a true data standard.

openIDS

openIDS (Open Insurance Data Standards) is that standard. It is the workhorse inside openIDL, creating the actual data models and specifications that allow insurers, regulators, reinsurers, and service providers to communicate seamlessly.

It provides a core insurance model (covering policy, claims, and risk) and extension points (for jurisdiction-specific regulations, emerging risks like climate or cyber, and industry innovations like IoT).

It is governed openly under Linux Foundation rules, ensuring neutrality, transparency, and long-term sustainability.

Understanding openIDL and openIDS

People sometimes get confused about how openIDL and openIDS relate to each other. Here’s the straightforward way to think about it:

• openIDL is the Linux Foundation project. It provides the overall umbrella — the legal home, the resources, and the organizational structure. It’s what makes this work possible in an open, neutral, and anti-trust compliant environment.
• openIDS (Open Insurance Data Standards) is the working group inside openIDL. It is where the community actually does the work:
  • Hosting the meetings.
  • Defining the standard.
  • Running the governance process around that standard.

In other words:

• openIDL = the umbrella + resources + home within the Linux Foundation.
• openIDS = the community that meets, builds, and governs the data standard under that umbrella.

Together, they make sure the insurance industry has both the structure to support long-term collaboration (openIDL) and the active community and standards development that drives real progress (openIDS).

The openIDS Homeowners Working Group has officially kicked off development of the Residential Structure Model v1.0, and momentum is strong. Building on the solid foundation contributed by Cloverleaf’s model, we’re now laser-focused on refining, enhancing, and filling any gaps needed to make this standard a robust, industry-ready resource.

Why the Residential Structure Model Matters

The Residential Structure Insurable Object Template provides a structured way to capture detailed information about an insured risk. In insurance, an “insurable object” is any item covered (or excluded) in a policy. By defining this model for residential structures, we are creating a common framework for insurers to record and analyze critical details about homes, their conditions, and their exposures.

This is essential for:

• Risk assessment – More accurate underwriting with standardized data.
• Regulatory compliance – Faster responses when regulators request information in familiar formats.
• Industry interoperability – Shared definitions that eliminate costly translation between company-specific models.
• Data-driven insights – Stronger analytics for trends, geographic risk analysis, and product development.

What’s Inside the Model

The Residential Structure Model v1.0 aims to define a wide range of attributes that help insurers evaluate residential risks. Some examples include:

• Construction and condition details – Construction type, siding, roof condition, plumbing, electrical, and wiring inspections.
• Occupancy and usage – Number of families, apartments, or household residents, weeks rented, and presence of businesses on premises.
• Risk-reducing and exposure factors – Security systems, sprinkler indicators, fire protection classes, alarm types, and distance to tidal water.
• Property value and features – Year built, purchase price, replacement cost, living area, garage/carport type, and swimming pool exposure.
• Special indicators – Vicious animal presence, ongoing additions, business at residence, or long-term residency.

This comprehensive approach ensures that insurers can capture not just the physical structure, but also its use, condition, and potential exposures.

Call for Collaboration

Right now, our priority is identifying and closing any gaps. This is where the community comes in. If you have resources, insights, or data that can strengthen the Residential Structure Model, your input is needed. Workstream members are providing industry standards/references the group can review and use to consolidate the gap analysis. Before our next meeting our plan is to:

1. Review gaps and determine additions or subtractions for openIDS Residential Structure model v0.1.
2. Align on standard naming conventions for openIDS Residential Structure model v0.1.
3. Confirm logical format/structure for openIDS Residential Structure model v0.1 and subsequent data standards.

Our next Homeowners Workstream call will be on Monday, August 25th at 2:30pm ET and open to anyone who wants to join us. Click here to access the Zoom link for our next meeting.

To get involved, please see:

• Artifact overview (model + tables)
• August 18th call recording and summary wiki 
• Zoom links to upcoming meetings:
  • Data Standards Working Group Monday, August 25 at 2pm ET
  • Homeowners Workstream Meeting Monday, August 25 at 2:30pm ET

The more perspectives and data we gather, the stronger and more adaptable this standard will become for the entire industry. We look forward to working together!