Why legacy systems are undermining insurance innovation

Angus Black, Director at BarnOwl Data Solutions 

In an era where data is driving everything from underwriting decisions to personalised customer engagement, one major obstacle still haunts the insurance industry, siloed legacy systems.  

Despite the proliferation of modern tools, cloud capabilities, and advanced analytics, many insurers continue to rely on a patchwork of outdated systems that fragment data, slow down operations, and create serious compliance risks. 

The issue isn’t just technical, it’s strategic. When data sits across multiple platforms in different formats, it leads to operational inefficiencies, delayed decision-making, and regulatory vulnerabilities. If these systems aren’t integrated or cleaned up, insurers can find themselves making high-stakes decisions based on inconsistent or incomplete data. 

The hidden cost of not solving system fragmentation – The cost of maintaining disjointed systems is threefold. First, there’s the immediate operational drag. Teams waste time manually reconciling data, resolving inconsistencies, and trying to piece together the full picture across multiple systems. That time could be better spent analysing trends, responding to customer needs, or improving performance. 

Second, there’s a strategic delay. Fragmented data leads to decision delays, because stakeholders must repeatedly validate and cross-check information before acting. Uncertainty in data means slower, more cautious operations, hardly a hallmark of a competitive, data-driven business. 

Finally, there’s a compliance and regulatory risk. Without a centralised, consistent source of truth, it’s difficult to prove data lineage, provide timely reports to regulators, or confirm that the data is accurate and trustworthy. Inconsistencies in reporting or submission gaps can raise red flags and damage credibility. 

How centralised data unlocks value – At BarnOwl, we focus on helping insurers consolidate their data into a single, governed environment. The value of this centralisation stretches across all functional areas. 

For underwriting, centralised data means underwriters can see a comprehensive view of each policy, including historical claims behaviour, rating factors, payment patterns, and risk exposures. This ensures more accurate pricing, better risk profiling, and a clearer view of the portfolio’s overall exposure. 

For claims teams, access to consolidated data helps with fast cover verification, fraud detection (such as duplicate claims or unusual payment patterns), and access to complete claims history. Automation can also be built around financial processes, such as funding floats and integration with financial systems, making claims handling faster and more accurate. 

For reinsurance teams, a clear, consolidated view of premiums and claims over time helps monitor exposure and ensure coverage thresholds are properly managed. This means over-limit exposures can be flagged in real-time, and reinsurance agreements can be continuously evaluated for performance and adequacy. 

From insights to action: operationalising data 

The final step of any data transformation journey is ensuring insights don’t just sit in dashboards, they need to drive action. That’s what we call “the last mile” of data transformation. 

Dynamic dashboards are the front line of this effort. Tailored to business needs, these dashboards track KPIs, SLAs, and other performance metrics in real time. Whether it’s loss ratios, policy cancellations, or large claims, the dashboards highlight what matters and make it easy for users to drill down and act. 

Next is data integration into downstream systems. By embedding clean, validated data into finance, reinsurance, claims, or policy administration systems, insurers streamline workflows and reduce duplication. Automated alerts and rule-based triggers also ensure that risks, anomalies, or missed thresholds are addressed quickly, helping insurers move from reactive to proactive operations. 

Enriching internal data with external sources – Internal data alone doesn’t always tell the full story. That’s where external data enrichment comes in, providing additional layers of insight that support better decision-making. 

Geolocation data, for instance, enhances risk profiling by identifying properties near flood zones, rivers, or high-crime areas. This information can be used to assess concentration risk, validate sums insured, and improve pricing accuracy. It can also support strategic growth by identifying underrepresented regions for targeted marketing. 

Identity verification tools reduce fraud and streamline onboarding by ensuring policies are issued to verified individuals or entities. And crime statistics, when layered over existing data, offer valuable context for underwriting and risk mitigation, both for the insurer and for educating policyholders. 

Putting data at the heart of strategy – All this transformation is only possible when insurers start to view data not as an IT problem, but as a strategic business asset. For this to happen, executive teams need to be brought into the conversation from the start. 

With clean, integrated data, executives can predict market shifts, react to trends faster, and make better-informed decisions. Automated systems reduce manual overhead, saving time and money while improving accuracy. And, perhaps most importantly, reliable data becomes the foundation for innovation, enabling scenario planning, forecasting, and long-term strategy development. 

At BarnOwl, our mission is to deliver business-aligned data solutions that help insurers unlock this value. It’s not just about dashboards or data pipelines, it’s about creating a trusted, governed, enterprise-wide asset that powers growth, innovation, and resilience. 

Because, when data is clean, centralised, and connected, it does more than support operations, it becomes the engine of strategic transformation. .