How can claims modernization improve the first steps of a claim?

A claims investigation can only move as quickly as the information supporting it. 

Claims teams often begin the process with information arriving through multiple channels, in different formats, and with varying levels of completeness. At FNOL, teams may need to review submissions, extract relevant details, create a structured claim record, and route the claim for processing. Further into the lifecycle, investigators may need to compare claim narratives, documents, photographs, repair estimates, police reports, or other supporting evidence. 

Every additional step spent collecting, organizing, validating, or comparing information adds work to the process. 

This is where claims modernization becomes an operational question: How efficiently does information move from the moment a claim is reported to the point where a claims professional can make an informed decision? 

For claims leaders, the opportunity is to improve how information enters the process, how it is structured and shared, and how easily investigators can use it. The objective is to reduce unnecessary manual work while giving claims professionals better information to work with. 

Where should claims modernization begin? 

It should begin with the information entering the claims process, particularly at FNOL. 

When claim information arrives in unstructured formats, intake teams may have to manually review submissions, extract relevant details, create claim records, and route them to the appropriate workflow. Improving information capture at this stage can reduce repetitive administrative work while giving downstream teams better-quality information. 

FNOL automation can support this part of the process by helping streamline submission review, information extraction, claim record creation, and workflow routing. 

The claims operations analysis cited in our playbook showed 25% faster claim intake and a 15% improvement in claim data capture accuracy through approaches focused on streamlining FNOL and structured information extraction. 

Why does claims data quality matter downstream? 

The quality and structure of information captured at FNOL can affect how easily downstream teams can work with a claim. 

Better-organized claim records can give investigators a stronger foundation for reviewing evidence, reduce unnecessary follow-up, and make relevant information easier to locate. Claims data quality therefore becomes an operational consideration across the claims lifecycle, rather than an issue limited to intake. 

The technology supporting this process also matters. Insurance claims processing software can help provide the workflows through which claim information is captured, structured, and routed, but the underlying information still needs to be complete and usable for downstream teams. 

The practical question is whether the information reaching the next team is structured well enough to support the work that follows. 

How can AI support claims investigation? 

Once a claim reaches investigation, the information challenge takes a different form. 

Investigators may need to review several sources of evidence and determine whether the details align. A claim narrative may need to be considered alongside supporting documentation, photographs, repair estimates, police reports, or other records. When this comparison happens manually, finding inconsistencies and validating information can take additional time and effort. 

Intelligent evidence analysis can support this process by bringing information from multiple sources together, helping investigators identify inconsistencies, and flagging areas that need further review. This allows claims professionals to spend less time locating and comparing evidence and more time assessing what the information means in the context of the claim. 

Our playbook reports 20% faster claims investigations and a 15% improvement in identifying inconsistencies across claim evidence from this type of modernization effort. 

What should claims leaders ask when modernizing claims operations? 

For teams assessing their claims modernization priorities, three questions can help identify where operational friction sits. 

1. How much manual work happens at FNOL? 

Look at the steps involved in reviewing submissions, extracting information, creating claim records, and routing cases. 

2. How complete and consistent is the claim information reaching downstream teams? 

Better-organized claim records can reduce unnecessary follow-up and give teams a stronger foundation for investigation. 

3. How efficiently can investigators review supporting evidence? 

Examine how much time goes into locating, comparing, and validating information across documents and other evidence sources. 

These questions also point toward a broader measurement framework. Our playbook recommends looking beyond processing speed to measures such as claim intake time, investigation turnaround time, manual effort per claim, claims data quality, customer responsiveness, and investigation consistency. 

Claims modernization can then progress through practical stages: simplify intake, improve data quality, strengthen investigations through intelligent evidence analysis, and continuously optimize operations. 

What is a practical approach to modernizing claims operations? 

For insurers evaluating where to begin, this provides a practical starting point. Follow the information, look at where it enters the claims process, how it is structured, how it moves between teams, and how easily investigators can use it. 

Our Modern Claims Playbook brings these ideas together into a practical framework for modernizing claims operations, with a focus on intelligent intake, information quality, evidence analysis, measurement, and continuous improvement. 

Read the full Modern Claims Playbook here.