Across the industry, the reinsurers that are getting real value from AI aren’t necessarily those using only most advanced tools. They’re the ones that have taken the time to get their data in order. “Data quality first” isn’t a tagline. It’s the foundation for any serious AI strategy.

The AI hype trap

Reinsurers have spent years and considerable amount of time and money in data & analytics platforms, governance structures and digital platforms. Yet many still struggle with fundamental questions. Can we trust the data?

Often, the answer is no.

Even with advanced systems in place, the same issues keep surfacing: inconsistent terminology, scattered documentation, siloed processes. AI doesn’t solve these problems magically. Though it can reveal them. Without a reliable data foundation, automation and intelligence doesn’t scale but it stalls.

Where data quality breaks down

There are some familiar culprits.

Language is often inconsistent. Different teams use different terms for the same concept, or worse, they use the same term to mean entirely different things. By adopting principles from Domain-Driven Design, we can establish a Ubiquitous Language – a shared vocabulary that aligns business and technical teams around common concepts, reducing confusion and enabling more accurate analysis.

Documentation is scattered. Critical documents, like final signed reinsurance contracts, are frequently buried in inboxes or outdated folders. Even well-organized systems like SharePoint often fail to provide the clarity or version control that teams need.

Processes remain manual. Underwriters still re-enter the same data across systems. Analysts compare spreadsheets to databases by hand. These workarounds create bottlenecks and leave room for errors.

In short, while technology has moved forward, the underlying data strategy hasn’t always kept pace. The result: more tools, but not necessarily more trust.

Speak the same data language

The first step in fixing this is straightforward but powerful: agree on what the data means.

A shared glossary of key terms isn’t bureaucratic overhead. It’s a strategic enabler. When everyone in the organisation understands what a term like “treaty”, “program” or “insured object” means, systems can align, reports gain clarity and AI models can operate without getting tripped up on semantics.

It’s not about rigid standards. It’s about creating a shared foundation that supports scale and consistency, no matter the use case or department.

Discoverability builds trust

In large organisations, data is naturally distributed. That isn’t the problem. The problem is lack of visibility. If teams don’t know where data lives, how it’s evolved or who owns it, they stop trusting what they see. And when that happens, they build their own local version of the truth, often in Excel.

If executives are relying on side spreadsheets to verify reports or track exposures, it’s a sign that core systems aren’t meeting expectations.

Manual work is a hidden cost

Whenever someone hunts for a contract version, reconciles data manually or re-enters values into different platforms, time and talent are being wasted. These tasks don’t appear on cost reports, but they add up. They’re a hidden tax on performance.
Technologies like RPA or AI might patch over the symptoms, but they don’t solve the root cause. The real solution is to build processes that don’t rely on workarounds in the first place.

Integrate quality into your organization

The best way to manage data quality is to address it early, but the approach varies depending on the size and complexity of the organization.

For smaller corporates, embedding quality at the point of entry means streamlining data capture processes and ensuring that accurate data is collected directly at the source. Connecting systems so that information flows smoothly between underwriting, claims, and finance can often be achieved with targeted integrations and simpler data models.

For larger corporates, the challenge extends across multiple domains, requiring a more comprehensive approach. Here, leveraging a common data model becomes essential, that connects disparate data sources and maintains semantic consistency across domains. Intelligent validation and contextual prompts guide users while they’re working, aligning data inputs with the established data model, not just after the fact.

The real competitive edge

At risktec, we work with reinsurers to move from fragmented landscapes and operations to AI-ready and integrated, cohesive environments. The approach is pragmatic: clarify and unify data sources, define and establish ownership, build and operationalize lean but effective governance, and create a scalable and flexible architecture. These may not be headline-grabbing actions, but they drive measurable value: faster insights, better decisions, and operational scale.

This isn’t about chasing trends. It’s about creating capability.

Final word

If AI is a serious part of your strategy, data quality needs to be more than an aspiration. It needs to be a priority.

Ask yourself: Do your teams trust the data? Can they find what they need, when they need it? Are your systems supporting them or forcing them to work around the gaps?

Make data quality your operating principle. Because AI doesn’t create competitive advantage on its own. What it does is help you unlock the value of something far more fundamental: data you can rely on.