Underwriting the Future: Data Maturity as a Carrier's Competitive Edge

The insurance industry runs on data. Every policy and claim, along with customer interactions, adds to a growing pool of information that — if matured — improves risk assessment, speeds up claims and delivers more personalized service. Yet for many insurers, that potential remains locked away in silos, outdated systems and inconsistent governance.

To realize the full value of this expanding data pool, insurers must move beyond traditional data management and embrace a more strategic, enterprise-wide approach. This transition is rooted in data maturity, a transformation that can empower an organization to evolve from reactive to predictive risk management, from generic to personalized service, and from follower to leader

The untapped advantage hidden in your data

As the insurance industry shifts from repairing losses to predicting and preventing them, properly governed real-time data becomes a strategic asset.  

Integrating internal signals with third-party sources, such as property characteristics, telematics and credit indicators, gives insurers a sharper lens into pricing, increases straight through processing (STP) and surfaces fraud earlier. These external insights also support more personalized products and more effective fraud detection. With this broader view, carriers can operate with greater efficiency and stay competitive in a data-driven market.

But none of this is sustainable without trusted data. The key is to elevate the data's quality, accessibility and governance so it can be trusted and acted upon. Without that foundation, even the most advanced analytics — and the AI models that increasingly rely on them — will deliver inconsistent or misleading results.

Governance that de-risks the business

Accuracy, auditability and transparency aren't just technical standards, but strategic requirements shaped by regulations such as the NAIC Model Laws, GDPR and state-level privacy statutes.

Typically, insurance carriers are required to maintain and use their data for actuarial modeling to build risk profiles and to analyze claims and fraud. This is worth restating: these results are only as accurate as the data they rely on. If data is incomplete, incorrect or potentially biased, it can introduce unnecessary risk. That's why insurers must understand data lineage, audit modified data, and proactively identify and correct bias.

For example, in a recent racial bias lawsuit, the Center for Race, Inequality, and the Law filed a groundbreaking lawsuit under the Fair Housing Act, alleging racial bias in insurance practices. This case is notable for using insurer-specific data to support claims of discrimination, marking a new legal frontier in data-driven bias litigation. The insurance sector should take the lead on ethical data use, especially given its heavy reliance on data for underwriting and claims. 

Beyond compliance, governance builds trust by assessing risk fairly and handling claims transparently while protecting customer data. It also reduces bias in predictive models, supporting ethical practices and better business outcomes.

Overcoming legacy constraints 

Having accurate, well-managed data is only part of the equation; integrating that data poses a separate challenge for insurers.

Many carriers operate platforms that are optimized for data storage rather than analysis, which hinders enterprise-wide data integration and delays actionable insights. While a full replacement can be costly and disruptive, inaction results in slower decision-making, reduced customer satisfaction, lower Net Promoter Scores and missed opportunities.

A better path is progressive modernization: integrate core systems via governed data products, automate pipelines and introduce common data definitions so underwriting, claims and actuarial teams work from the same truth. This approach activates data and value fast, while modernizing the foundation.

Case in point: A global insurer operating in over 170 countries partnered with WWT to modernize its infrastructure by deploying Splunk across the enterprise. This strategic move enabled real-time visibility into IT operations and security, powered by advanced analytics. As a result, the organization achieved greater operational efficiency, strengthened compliance and improved its ability to act on data-driven insights.

Chart your next step with WWT's Data Maturity Model

Our WWT data experts developed a data maturity model to help organizations assess their current state and prioritize the next steps across governance, integration and access. While advanced capabilities like AI often steal the spotlight, they're not the starting point; they emerge from a strong, trustworthy data foundation. 

For some insurers, that foundation may support deploying anomaly detection to flag suspicious claims. For others further along the curve, it could enable real-time decision engines or generative AI to enhance customer interactions. 

Regardless of AI ambitions, progress along the data maturity curve improves reporting and operations while enabling faster, more informed decisions. And without trustworthy data, even basic improvements are compromised.

WWT's Data Maturity Model

Here's a closer look at how WWT's Data Maturity Model applies within the insurance landscape.

Model overview
Level 1: Initial — Siloed data, manual reporting and minimal governance
Level 2: Developing — Pilot‑level integrations and early governance
Level 3: Defined — Centralized platform, automated pipelines and formal governance
Level 4: Managed — Enterprise-wide access, formalized policies and real-time pipelines
Level 5: Optimized — Fully integrated, governed and automated environment

Examples by level
* L2 ➜ L3: Unify property and claims data to improve subrogation and avoid leakage.
* L3 ➜ L4: Implement real-time event streams to lift straight‑through processing (STP) for low-severity claims.
* L4 ➜ L5: Add transparent model monitoring and bias checks across pricing models.

Across underwriting, claims and actuarial, advancing one level typically yields higher STP, shorter claims cycle times and tighter model governance — the foundations that make AI dependable and auditable. If you're at L2 or L3, the fastest wins come from standardizing definitions and automating pipelines; at L4 and beyond, bias monitoring and transparent model ops keep you compliant as you scale.