Real Time Fraud Detection Starts When Every Claim Can Be Investigated
For years, insurers have talked about real time fraud detection as if the answer were simply better scoring models, more rules, or sharper alerting. Those tools matter. But in practice, they only solve part of the problem.
The harder part begins after a claim is flagged, or more often, after it is not.
That is because most of the evidence that matters in fraud review does not live neatly inside structured fields. It lives in claim files, medical records, examiner notes, correspondence, provider histories, public web results, court records, social activity, and third-party data systems. Investigators know how to use that information. The problem is that doing so thoroughly is slow, manual, and expensive.
As a result, most carriers do not apply true investigative scrutiny to every claim. They cannot. They reserve that effort for a relatively small subset of files that look suspicious enough to justify the time.
That is the gap Nomad Data is focused on closing.
With Nomad Data’s Doc Chat, real time fraud detection becomes less about static alerting and more about scalable investigation. Instead of relying on a handler, examiner, or SIU investigator to manually jump across systems, search the web, check provider information, review timelines, and piece everything together, Doc Chat can execute a carrier-specific investigative workflow that brings those steps together in one auditable process.
The result is not automated claim denial. It is faster, broader, and more explainable fraud review.
Insurance fraud remains a major business problem. The FBI has long estimated that non-health insurance fraud costs more than $40 billion per year, while the Coalition Against Insurance Fraud has estimated total U.S. insurance fraud across sectors at $308.6 billion annually. For carriers, that makes better detection, earlier review, and more scalable investigation a strategic priority, not just an operational one.
As Nomad Data CEO Brad Schneider puts it:
“Real time fraud detection should not stop at alerting. The real value comes from helping carriers investigate faster, verify more, and give adjusters a clearer picture of what is actually happening in the claim.”
The Real Bottleneck in Real Time Fraud Detection
Insurers already know many of the patterns associated with fraud. SIU leaders and fraud investigators have documented red flags for years. Claims executives understand where leakage occurs. Examiners know the warning signs that make a claim feel off.
But knowledge alone does not create real time fraud detection.
Execution does.
In the real world, even experienced investigators are constrained by time. When a disability or long-term care claim arrives, a reviewer may need to assess not only what is inside the file, but whether that file matches what can be verified outside it. That could mean comparing a medical chronology against public web results, checking whether a provider is legitimate, looking for known fraud history tied to a doctor or lawyer, or reviewing public activity for inconsistencies.
Today, that work is usually done manually, and only on selected claims.
An adjuster or investigator logs into multiple systems. They search for social profiles. They visit different platforms. They scroll through timelines, review comments, verify providers in outside databases, check medical context, look for court rulings, and try to connect the dots. When they do find something important, they still need to summarize it and explain why it matters.
That is skilled work. But it is also slow work.
And when investigative work is slow, real time fraud detection remains narrow by definition. A carrier may be excellent at investigating a few claims deeply while leaving many others with only surface-level review.
This is exactly why many fraud programs hit a ceiling. The issue is not a lack of intelligence or experience. It is a lack of scalable investigative capacity.
Why Real Time Fraud Detection Matters More Than Ever
The need for stronger real time fraud detection is only growing. Fraudsters are not standing still, and carriers are facing increasing pressure to process claims quickly while still controlling leakage. At the same time, many insurers are evaluating AI for fraud detection because they need better coverage across the claims lifecycle and more leverage for their human investigators. Deloitte has noted that AI can help free investigators to focus on more complex cases, but also warns that insurers need transparency and accountability in these workflows, especially in regulated, high-stakes decisions.
That combination matters.
Carriers need speed, but they also need defensibility. They need automation, but not a black box. They need earlier signals, but they still need humans in control.
That is why the conversation around real time fraud detection is shifting. The goal is no longer just to identify suspicious claims faster. The goal is to investigate more claims, with greater depth, while preserving explainability and human oversight.
Brad Schneider captures that tension well:
“Insurers do not need another black-box score. They need a way to operationalize investigation at scale, with outputs an examiner can trust, review, and act on.”
Why This Matters in Long-Term Care & Disability Claims
This challenge is especially clear in long-term care and disability claims.
These files are often document-heavy, medically complex, and extended over long periods of time. They may involve years of treatment records, changing medical narratives, outside specialists, legal involvement, and repeated examiner review. They are exactly the kind of claims where inconsistencies can matter enormously, but also where those inconsistencies can be hardest to detect.
A claimant may report severe impairment, limited mobility, or debilitating pain. The file may support ongoing payments. The medical story may appear settled.
But there can still be red flags.
In some cases, a deeper investigation reveals a very different picture: public content showing extensive travel, physically demanding activities, or a lifestyle that does not align with the claimed limitations. A claimant who appears unable to function normally in the claim file may appear in public posts hiking, traveling internationally, or participating in endurance activities.
That does not automatically prove fraud. It should not. Human review still matters.
But it is exactly the kind of discrepancy that should surface early in a strong real time fraud detection workflow.
The problem is that most carriers cannot afford to perform that level of manual research on every file. They do it on the claims that already look suspicious, not on the claims that merely have hidden inconsistencies waiting to be discovered.
From Document Chat to Investigative Workflow
What makes Nomad Data’s approach different is that Doc Chat is not positioned as a generic chatbot or a one-size-fits-all AI assistant.
It is a highly customizable, agentic system designed around the carrier’s own workflow, data sources, and investigative style.
That matters because no two insurers investigate exactly the same way. Carriers have different lines of business, different external data relationships, different fraud patterns, and different operating preferences. One carrier may want strong provider verification. Another may care more about public web research, court rulings, or lawyer-related patterns. Another may want a specific workflow for disability claims or long-term care claims.
Doc Chat can be configured around those needs.
It can pull from the claim file and uploaded documents inside Nomad Data. It can connect to the web. It can support provider verification. It can connect to third-party data sources. It can follow a carrier-specific sequence of checks designed to mimic the same type of investigation a strong claims or SIU team would perform manually.
In other words, it supports real time fraud detection not by replacing investigators, but by making their method scalable.
As Schneider explains:
“It is not a one-size-fits-all tool. Every carrier has its own investigative workflow, its own data sources, and its own definition of what matters. The platform needs to adapt to that reality.”
That ease of use matters just as much as the underlying intelligence. A handler can click a button and have a claim examined. A carrier can also set the workflow to run in batch mode, so every claim is automatically analyzed and a report is produced when issues are found.
That changes the coverage model completely.
A Practical Example of Real Time Fraud Detection
Consider a disability claim entering review.
The claim file includes medical records, internal notes, provider statements, prior correspondence, and the claimant’s stated limitations. In a traditional workflow, an examiner may read the file, form an initial impression, and move on unless something obviously triggers further investigation.
With Nomad Data’s Doc Chat, that same claim can go through a much deeper first-pass review.
The system can analyze the medical chronology and identify the claimed functional limitations. It can compare those limitations against statements elsewhere in the file. It can verify provider identities and specialties. It can check relevant fraud-related provider data sources. It can search the web for public signals tied to the claimant, providers, lawyers, or related entities. It can compare what appears in public timelines against what the claim and medical documents say.
If it finds inconsistencies, it can present them in a structured report.
That report does not simply say that something looks suspicious. It shows the examiner what it found, where it found it, and why it may matter. It can cite the claim file, cite the medical record, and cite the external source material. It can summarize the discrepancy and provide an audit trail for follow-up.
That is what real time fraud detection should look like in practice: not a black-box score, but a fast, explainable investigation starter.
Not Just Aggregation. Verification.
A major distinction here is the difference between summarizing information and verifying information.
Many tools can aggregate content. Fewer can help carriers test whether the claim story holds up against the broader world.
That is where the value of this approach becomes much more strategic.
Fraud detection has always depended on comparison. Does the timeline make sense? Does the provider fit the claimed treatment? Does the public record align with the medical narrative? Does this legal representation pattern resemble known issues? Does the external evidence confirm, contradict, or complicate what is in the file?
Those are verification questions.
Doc Chat enables real time fraud detection by helping carriers marry what is in the claim file with what can be found and validated outside the claim file. That broader lens is what allows red flags to surface sooner.
And sooner matters. The earlier a red flag is identified, the more options a carrier has. The examiner can investigate while the file is still active. SIU can engage earlier. The organization can avoid paying out blindly for months or years before a manual review finally uncovers the inconsistency.
Explainable, Auditable, & Built for Human Judgment
For fraud workflows, speed is valuable. Explainability is essential.
No insurer wants an opaque system making silent conclusions in a high-stakes claims process. And Nomad Data is not trying to create one.
The role of AI here is to gather, compare, verify, and summarize information for the adjuster. The final decision remains with the human reviewer.
That governance model is critical to trustworthy real time fraud detection.
Doc Chat is designed so the sources used are configurable. Carriers can decide what external systems and data sources should be included. They can shape the workflow to fit their operating standards. They can determine how aggressive or narrow the initial review should be.
Just as important, the outputs are explainable and auditable. The system can provide a full readout of what it examined, what it found, and where each finding came from. Instead of forcing adjusters to search multiple sites and manually compile evidence, it returns a summarized report with citations back to source material.
Deloitte has pointed out that transparency and accountability are central concerns as insurers scale generative AI in claims, underwriting, and fraud detection. That makes explainable workflows especially important for carriers that need not only speed, but also defensibility for regulators, auditors, and internal stakeholders.
As Schneider puts it:
“The AI is not making any decisions. The AI is gathering information, comparing evidence, and organizing it so the adjuster can make a better decision faster.”
That is the right model for insurance. Better tools should expand expert judgment, not replace it.
Why This Changes the Economics of Fraud Review
The biggest business impact may be simple: carriers can afford to investigate more claims, more deeply.
That is the hidden promise behind better real time fraud detection.
Historically, there has been a clear tradeoff. A carrier could scrutinize a few claims intensely or many claims lightly. Manual investigation made it almost impossible to do both.
AI-based investigative workflows change that equation.
When Doc Chat can perform the first-pass research across every claim, carriers are no longer limited to applying fraud-style investigation only to the files that already look bad. They can apply deeper scrutiny broadly, then use human expertise where the system finds meaningful issues.
That drives faster investigations, but speed is only part of the outcome.
It also expands detection coverage. It increases the likelihood of discovering fraud. It surfaces red flags earlier. It reduces the need for adjusters to do clerical research across many disconnected systems. And over time, it can create substantial cost savings by reducing leakage that would otherwise go unnoticed.
For SIU leaders, this means better use of scarce investigative resources. For claims executives, it means a more scalable control layer across the portfolio. For fraud investigators, it means less time spent gathering obvious facts and more time spent evaluating the cases that genuinely require expertise.
This matters because the broader fraud burden is significant. The Texas Department of Insurance cites the FBI estimate that non-medical insurance fraud is at least $40 billion each year, and also notes Coalition Against Insurance Fraud data suggesting fraud occurs in about 10% of property-casualty losses. Whether an insurer is focused on disability, long-term care, workers’ compensation, or P&C, more scalable review has direct financial implications.
Real Time Fraud Detection Needs Third-Party Data
One of the biggest limits of many AI fraud tools is that they only work inside the four corners of the claim file.
That is not enough.
The most effective real time fraud detection strategies connect internal documents with external verification sources. Depending on the workflow, that may include provider data, public web results, sanctions or licensing checks, court records, address verification, death file checks, or other third-party datasets that help a carrier validate what it is seeing.
This is where Nomad Data has a meaningful advantage.
Doc Chat is not just reading documents in isolation. It can be configured to bring in external context and third-party data sources as part of the investigation. That gives carriers a much more complete picture and makes the workflow more useful in real-world fraud scenarios.
Brad Schneider puts it simply:
“Fraud detection gets stronger when you can connect the claim file to the outside world. Documents tell part of the story. Verification tells you whether the story holds up.”
That is an important distinction for buyers. Plenty of tools can summarize a PDF. Far fewer can support a broader, carrier-specific investigative process that blends claim evidence with outside verification.
What Insurers Should Look for in a Real Time Fraud Detection Platform
For carriers evaluating solutions, the question is not just whether a tool can flag suspicious claims. The question is whether it can support how fraud work actually happens.
A strong real time fraud detection platform should do a few things well:
1. Analyze document-heavy claims
Fraud often hides in large, messy files. The system should be able to process medical records, correspondence, notes, timelines, and supporting documents at scale.
2. Support external verification
Fraud review should not stop at the claim file. The platform should be able to incorporate web research, third-party data, and other outside signals.
3. Produce explainable outputs
Investigators and adjusters need to see what was found, where it came from, and why it matters.
4. Fit carrier-specific workflows
Every insurer has its own priorities, thresholds, and investigative patterns. A generic assistant is not enough.
5. Keep humans in control
The best systems accelerate expert judgment. They do not replace it.
That is the lens through which Nomad Data approaches the market.
The Future of Real Time Fraud Detection
The insurance industry does not need more disconnected point solutions that summarize a document and stop there.
It needs workflows that reflect how fraud is actually investigated.
That means starting with the claim file, but not ending there. It means connecting internal evidence to external verification. It means applying carrier-specific investigative logic at scale. It means making deeper scrutiny possible on every claim, not just a small sample. And it means doing all of that in a way that remains explainable, auditable, and controlled by human decision-makers.
That is the future Nomad Data sees for real time fraud detection.
Not a world where AI decides claims.
A world where every adjuster, examiner, and investigator can begin with a richer, faster, and more complete picture of the truth.
When that happens, fraud review stops being a narrow manual function and becomes a scalable operational capability.
And that is when real time fraud detection starts to deliver on its name.
