How AI Turns Claim Files into Real-time Fraud Signals

Nomad Data

September 10, 2025

Fraud in insurance is no longer the same problem it was a decade ago. The cost of fraud is rising dramatically, and with it the complexity. Fraudsters are no longer relying on crude tactics like reusing the same photos or submitting handwritten invoices. They now have access to the same artificial intelligence technologies that insurers themselves are beginning to adopt.

That means fabricating medical evidence, creating synthetic documentation, and even crafting highly polished narratives to support illegitimate claims has become easier, faster, and more convincing. “The sophistication of the attacks is rising dramatically,” explains Brad Schneider, CEO of Nomad Data. “People can fabricate evidence of damage, let’s say, to an automobile, or more accurately articulate falsehoods that compose a claim. That’s becoming a much larger challenge.”

The trouble is, the strongest fraud signals are still buried in the claims files themselves. While structured data like names, dates, and providers can be scored against existing rules and models, the unstructured data inside PDFs, scans, medical notes, and attachments remains largely invisible to traditional systems.

This is where Nomad Data’s Doc Chat is changing the equation. “If your claims data lives in documents, your best fraud signals are hidden in plain sight. Doc Chat can surface them in minutes,” Schneider says.

By turning those hidden files into instant, real-time fraud signals, insurers can finally fight fraud at the same level of sophistication fraudsters are now operating.

The Problem with Claims Data

Claims fraud detection has always been a race between manual review and clever deception. Most insurance carriers are drowning in documentation: PDFs of repair invoices, scanned medical forms, lengthy physician notes, attachments from third parties, and legal correspondence. Each file is dense, unstructured, and difficult to parse at scale.

Fraudsters know this. They exploit the gaps by generating huge volumes of fabricated documents that overwhelm human adjusters. “You can fabricate enormous volumes of documents, medical records, legal forms — all of this is highly unstructured,” Schneider explains. “Traditional machine learning models struggle to find issues within these claims. And as fraudsters generate bigger and bigger volumes, you would need more and more sophisticated claim handlers to keep up.”

Manual review is slow, inconsistent, and expensive. Even a well-trained investigator can spend days poring over a single claim file, and still miss subtle patterns that link one case to another. Fraudsters thrive on these blind spots, knowing that disconnected files rarely get cross-referenced in real time.

The result: inflated claims slip through, repeat offenders go undetected, and the cost of fraud continues to rise.

Real-time AI Fraud Detection for Insurance

Traditional scoring models and rules-based systems provide some level of fraud detection, but they are fundamentally limited. They work primarily on structured data: who the parties are, which doctors are involved, when the claim was filed, or the frequency of claims over time.

But as Schneider notes, “They don’t excel at understanding what’s in a document. They can’t really understand the nature of an injury, how it’s described, or how an accident is described. It’s very hard for them to reach into the documents to do that kind of fraud detection.”

Real-time AI changes the game by doing exactly that. Instead of waiting for static fraud scores, adjusters can surface red flags within minutes of uploading claim files. Patterns, inconsistencies, and contradictions that once took days of manual investigation are uncovered instantly.

For claims teams, this means:

‍Speed: Investigations accelerate from days or weeks down to minutes.
‍Accuracy: AI can process the fine details of unstructured documents without fatigue or oversight.
‍Consistency: Every claim is reviewed against the same standards, eliminating variance across adjusters.
‍Proactivity: Fraud detection shifts from reactive audits to real-time intervention at the point of claim.

With fraudsters now leveraging AI to fabricate claims, insurers can’t afford to rely on outdated detection tools.

How Doc Chat Works

Nomad Data built Doc Chat to close this gap between unstructured claim files and actionable fraud detection.

The process begins by sitting down with adjusters and fraud professionals to understand the historic patterns they’ve seen and the supplemental information they need to spot fraud. From this, Nomad Data develops a custom AI “preset” — for example, a medical fraud preset that has been trained to read tens or even hundreds of thousands of pages for suspicious patterns.

From the investigator’s point of view, the workflow is simple:

‍Upload the claim files. PDFs, scans, handwritten or typed forms are all ingested.
‍Select the preset. For example, a medical fraud preset, property fraud preset, or underwriting preset.
‍Get an instant report. Doc Chat scans the entire file set and surfaces red flags, inconsistencies, and anomalies.

What once felt like “searching for a needle in a haystack” becomes a streamlined workflow. Investigators no longer have to read thousands of repetitive or boilerplate documents. Instead, they receive a clear set of flagged issues that warrant deeper review or additional documentation requests.

Critically, every answer Doc Chat provides comes with page-level citations. Adjusters and investigators can see exactly where the AI surfaced a red flag, ensuring the findings are audit-ready and defensible.

Another powerful capability is the Red Flag Feature. Carriers can embed their own rules and red flags into Doc Chat, ensuring that every adjuster applies the same best-practice standards consistently. Instead of relying on memory or experience, Doc Chat institutionalizes the latest fraud detection patterns and makes them available at scale.

As Schneider explains, “It doesn’t require you to educate people on all these fraud patterns, it just requires you to educate the system. As new ones emerge and it’s re-educated, it makes the handlers aware of them. It always ensures that every claim is being looked at with the most up-to-date fraud detection patterns.”

Claims Fraud Detection Examples

Fraud manifests differently across insurance lines, but the underlying challenge is the same: unstructured documents hide the most important signals. Doc Chat surfaces these in real time.

Medical Records:
- Identical diagnostic notes repeated across different patients’ claims suggesting fabricated or boilerplate documentation. Treatments prescribed that don’t align with the type of injury claimed, a subtle but telling indicator of fraud.
Underwriting:
- Applicants omitting prior losses that appear in supporting documentation, revealing discrepancies between application forms and historical records.

Ask Doc Chat This

Sample Prompt	What it Surfaces	Why it Matters
Scan this claim file set and list every inconsistency between medical notes, billing codes, and reported injury details. Cite page and line.	Cross-document contradictions, code mismatches, conflicting narratives	Flags fabricated or boilerplate documentation early and with citations for auditability
Compare provider names, addresses, and tax IDs across these claims. Highlight any overlaps with past suspicious cases or sanctioned entities. Cite pages.	Reused providers, shell entities, sanction hits	Exposes organized fraud rings and repeat offenders
Identify treatments or procedures that are not clinically aligned with the diagnosed injuries in these records. Provide rationale and citations.	Medically implausible care patterns	Detects padding and upcoding that inflate loss costs
Find all references to prior losses or pre-existing conditions across this applicant’s documents and note any omissions from the application form. Cite pages.	Undisclosed history, misrepresentation at underwriting	Improves risk selection and reduces adverse selection
Create a red flag summary using our firm’s rules: duplicate images, altered metadata, language anomalies, and identical phrasing across files. Include confidence and citations.	Custom rule hits, document forensics, linguistic clones	Standardizes best practices across adjusters and supports SIU escalation

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These examples highlight how Doc Chat doesn’t just find isolated issues within a single file. It can cross-reference claims across an entire book of business, detecting patterns that no individual adjuster could spot on their own.

Instead of relying on memory or intuition, adjusters gain a real-time fraud detection engine that consistently uncovers red flags, regardless of claim size, complexity, or volume.

Closing

Insurance fraud has always been costly, but the stakes have never been higher. Fraudsters now have access to AI tools that make their tactics faster, smarter, and harder to detect. The industry cannot afford to respond with outdated approaches.

Nomad Data’s Doc Chat provides a way forward. By transforming unstructured claims data into real-time fraud signals, it enables carriers to act faster, save costs, and apply the most advanced detection methods consistently across every case.

As Schneider puts it: “Given that fraudsters are using AI to go after insurance companies, it only makes logical sense for insurance companies to arm themselves with the same tools to detect this kind of fraud. To wait, to ignore this type of behavior, is tantamount to exposing yourself to enormous potential risk.”

The future of insurance fraud prevention lies in making the invisible visible — instantly. And with Doc Chat, insurers finally have the tools to do just that.

‍Learn more here.

FAQs

How does AI find fraud in claim files? It scans PDFs, scans, and notes to flag contradictions, reused text, or unusual treatments.
What makes Nomad's Doc Chat different from rules-based tools? It reads the whole document, not just names and dates, and shows flagged issues with citations.
Can we add our own fraud rules? Yes. You can embed custom red flags and update them as patterns change.
Will Nomad's Doc Chat speed up investigations? Yes. Reviews that take days can be done in minutes with page-level evidence.
Does it work on messy scans or handwritten forms? Yes. Doc Chat handles unstructured documents and highlights low-confidence spots.
Is Nomad's Doc Chat secure for sensitive data? Yes. It’s built for regulated industries, with strong privacy and audit-ready outputs.
How fast are results? Most claims return red flags within minutes of upload.
Can underwriting use Nomad's Doc Chat, too? Yes. It checks disclosures and supporting docs to catch misrepresentation.