The Critical Role of Data Quality in Claims Systems

May 28, 2026 / 5 min read

The insurance industry has invested heavily in sophisticated analytics platforms, fraud detection tools, and shared databases designed to surface risk and identify suspicious activity. Yet one foundational truth remains unchanged: analytics are only as good as the data that feeds them.

Before joining Verisk, I likely would not have fully appreciated the importance of clean data or the downstream impact it has on nearly every aspect of the claims process. During my time working in a special investigations unit (SIU), my focus was squarely on claims investigations: reviewing referrals, identifying suspicious behavior, and determining next steps. Data quality simply wasn’t on my radar.

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Like many claims professionals, I relied heavily on Verisk ClaimSearch^® and assumed the system contained all relevant data available. What I didn’t fully appreciate at the time was that the scope of data transmitted from my carrier’s claims system into ClaimSearch reflected the fields configured and enabled at the time. As technology evolved and new data fields were created, the transmission mappings weren’t always updated to include them. As a result, some potentially useful information never made it into the database I was relying on—information that, some cases, could have meaningfully impacted my investigations, particularly when trying to connect related claims or identify potential fraud rings.

Now that I work closely with many carriers, I see firsthand how data is submitted in a variety of ways— and how carriers can unknowingly leave significant analytical value on the table simply because optional, high-value data fields aren't being utilized.

The evolving landscape of claims data

Historically, most industry data sharing focused primarily on structured information: names, addresses, vehicles, providers, and claim details. Today, the ecosystem has expanded beyond traditional fields. Carriers can now submit images through ClaimSearch, enabling photos associated with claims—vehicle damage, documents, property conditions—to be shared across the industry alongside structured data.

This is a significant step forward for fraud detection and analytics. Images provide powerful context during investigations and can help identify connections that may not be obvious through text data alone.

But images are more valuable when supported by accurate and complete data. A photo of vehicle damage becomes far more useful when it is linked to the correct VIN, claim number, claimant information, loss date, and location. Without that structured data associated with the image, the ability to connect that image to other claims or patterns becomes significantly limited. The expansion of image submissions makes high-quality data more important than ever.

Why data quality matters more than ever

As analytics now play a significant role in the initial triage of claims, the importance of clean, structured data has become impossible to ignore. Analytics tools help identify patterns and surface potential risks earlier in the claims lifecycle—but they’re designed to assist, not replace, experienced professionals. Adjusters and investigators remain responsible for reviewing claims, evaluating concerns, and determining the appropriate course of action.

That reality makes data quality a direct enabler of both technology and talent. Clean, consistent data makes it easier analytics systems, and the people who ultimately handle the file, to do their jobs effectively.

For example, at first notice of loss (FNOL), there is often only minimal information available. But as additional details are gathered (vehicle information, claimant identifiers, emails, medical providers, addresses), those updates can dramatically improve the ability to identify related claims, patterns, or even rings targeting a carrier. If those updates aren’t transmitted promptly or consistently, critical connections may be delayed or missed all together.

The vendor partnership: keeping claims system integrations current

Maintaining high-quality submissions requires active, ongoing collaboration between carriers and their claims system vendors. Too often, data integrations are treated as “set it and forget it” implementations—some of which may have been configured decades ago and are overdue for review.

In practice, these integrations require ongoing oversight. Through working with numerous carriers to review and improve their analytic results and submissions—and comparing those submissions with their resulting matches—several common themes consistently emerge.

Based on those experiences, the following checklist can help carriers ensure their claims systems are properly aligned.

Accurate field mapping: Internal claims system fields must map correctly to data elements, so that information is transmitted with the correct meaning and structure.
Review required fields and optional fields: Fields identified as “required” should be technically enforced within the system—not simply marked as recommended or optional. Carriers should also confirm with their claims system vendors whether optional fields are being transmitted, as many of these fields provide significant analytical value.
Correct data format and structure: Dates, names, identifiers, and addresses should follow consistent formatting standards to ensure reliable matching.
Monitoring vendor updates: System upgrades, configuration changes, and vendor releases can unintentionally affect field behavior or data transmission.
Post-change testing: Any claims system change that affects claims intake, data fields, or integrations should include validation testing to ensure submissions remain accurate and complete.

Taking these steps helps ensure that improvements or updates to claims systems do not inadvertently reduce the quality of industry data contributions.

Building a culture of data discipline

Technology alone can’t solve the data quality challenge. The most effective insurers treat data discipline as part of their claims culture.

This includes:

Designing claims workflows that support accurate data capture
Training adjusters on the downstream importance of structured data
Regularly auditing claims submissions and match performance
Partnering with system vendors to continuously refine integrations

When data quality becomes a shared responsibility across operations, technology teams, and vendor partners, the benefits extend far beyond a single database.

The bottom line

As the insurance industry continues to advance through analytics, automation, and shared intelligence platforms, one thing remains constant: Data quality is the foundation that makes it all possible. Clean, consistent, well-structured data enables stronger analytics, more accurate industry matching, and ultimately more effective claims management and fraud detection. As new data types like images become part of the claims ecosystem, the importance of strong underlying data only grows.

The question for carriers isn’t whether analytics and industry intelligence will shape the future of claims—they already are.

The real question is: Is the data feeding those systems strong enough to support the decisions being made from it?

Learn more about how Verisk’s comprehensive solution suite is designed to help detect fraud faster.