Agentic AI claim processing: from hours of manual review to minutes of verified analysis

According to the 2025 State of Claims report that 41% of survey respondents say that at least one in ten insurance claims are denied, creating a huge administrative burden and putting pressure on the healthcare system. Of those denials, 26% are said to arise from inaccurate or incomplete data collected at patient intake, errors that could be caught earlier in the process and which cause further chaos downstream.

This challenge is also seen in the insurance company, with additional administrative overhead added when patients request reevaluation, which triggers a second review cycle, additional paperwork, and further strain on an already stretched operations team.

The best scenario possible is to process claim fast, with no errors, so it is clearly approved or clearly denied, based on complete information, with no ambiguity left for appeals to exploit. And that is where Vstorm entered to help.

The complicated case of healthcare insurance

The claim processing in healthcare, especially in accidents, requires a lot of manual, complicated work. Insured individuals need to deliver an exhaustive list of documents, digital and physical alike. These include, but are not limited to:

• The original insurance policy, where conditions, limitations and coverage are specified
• Medical documents that provide detailed information on the procedures performed
• Detailed descriptions of related accidents
• Any additional documents where applicable, such as a police report or confirmation that a particular accident had actually taken place

Within these documents, it is necessary to find several pieces of interconnected information which provide a basis on whether the insurer is going to pay for the procedure, or not. At a high level, the processing looks as following:

• Checking the time of the accident and whether the insurance was active at the time given
• Checking if the procedures the patient received were covered and are in the limits of the insurance
• Checking possible exclusions and additional terms and their possible impact on the payment

The challenges

This process sounds straightforward, but there are challenges along the way that make it difficult to process manually and which result in the 10% of false-positive denials mentioned above.

Extracting dates

There are many dates to extract from documents, and even more of them are included and make the image foggy. For example, the documentation has a date of the accident included, along with the birth date of the patient, and the starting and ending dates of the insurance. Also, timestamps from systems and emails, visit dates, and other information were included.

The problem gets even more complicated if one has multiple insurance policies overlapping, for example one private, one from their employer, and one provided by a veteran program.

Identifying which dates are relevant, and in what relationship to each other, requires careful, structured reading. A human reviewer doing this at volume, across dozens of claims per day, is exposed to compounding fatigue risk.

Tackling the handwriting and messy papers

The dates are delivered in both digital documents (for example a copy of the insurance policy) and physical forms completed by hand. And handwriting itself can be tricky, misleading and often illegible. At this point it is easy to confuse, for example, a flat 7 with a 1, and make comparable mistakes.

The papers themselves are also not always in a perfect shape. They can be slightly torn or creased, printed on low-quality paper, or more. Each increasing the chance of a misread that rolls through the rest of the review.

Spotting exclusions

Depending on the insurance, there are multiple exclusions and cases that need to be taken into the account when deciding whether to pay full cost of treatment or not. A common exclusion for example is not taking responsibility for accidents, as when an accident occurred while driving under the influence. It is also common to not pay for treatments if standard medical procedure order was not kept, for example, one should not take an X-ray without a direct request from a qualified medical professional.

This is contextual, policy-specific work that cannot be handled by rule-based systems alone.

Multimodal analysis

Last but not least, the documents include multiple modalities. Apart from the text, claims documentation can include images, graphs, and occasionally maps, particularly when the insured party is demonstrating that emergency transport was necessary.