The New Face of Insurance Fraud Looks Just Like a Legitimate Claim
Every week, my team reviews claims that look, on the surface, like open-and-shut cases. The injury is plausible. The treatment records are in order. The documentation is clean. And increasingly, that’s precisely the problem.
The question we’ve had to learn how to ask is whether it’s actually real or just posing as legitimate enough to move through the system. In today’s fraud environment, the line between the two is blurring fast.
I’ve spent most of my career in technology and analytics, and most recently in fraud-fighting efforts. I’d argue that the current moment is the most consequential shift I’ve seen in how fraud operates. Three forces are converging at the same time: AI tools that make document fabrication inexpensive and convincing; digital claims workflows that compress the window between submission and payment; and increasingly coordinated fraud networks that have mapped the detection gaps and built their schemes around them.
The result is fraud designed to pass traditional legitimacy tests on an individual claim basis — at first notice of loss, in the treatment records, in the billing and in the documentation. Complex fraud may only become visible when patterns are identified across claims.
The Pattern Problem
Here’s what this looks like in practice. A medical billing provider serving hundreds of claimants across multiple locations may not look unusual on an individual claim. A roofing contractor whose scopes consistently expand beyond verified damage may not surface as a problem on any single file. An attorney appearing repeatedly in claims across different businesses and geographies may be invisible unless you’re looking across your entire book.
The characteristics that make vendor and provider networks efficient — scale, specialization, distribution relationships — are the same characteristics that make them difficult to monitor closely. Complex schemes share a common design principle: they may produce plausible individual transactions that only reveal their true nature at the pattern level.
Consider this example of a 14-claim staged accident ring my team recently identified. No single claim triggered a flag — each looked like a routine fender-bender with legitimate injuries, treatment records and documentation. The fraud only became visible when investigators mapped relationships across all 14 claims and found the same attorney, the same medical providers and some of the same “victims” appearing repeatedly. Each individual transaction seemed plausible, yet the pattern was unmistakable.
Detection Timing Matters
When I talk to risk managers and business leaders about fraud, the conversation often focuses on case studies — schemes that were caught and resolved. Those stories matter. But the more important question is about capability: what does your carrier’s detection infrastructure actually look like, and at what point in the process does it engage?
There are three things that separate those carriers that are built for today’s fraud environment from those who are not.
Integration between analytics and investigation. The organizations catching coordinated fraud schemes early aren’t doing it with investigators or analytics alone — they’re doing it with both working in combination, from the moment a claim enters the system. Analytics surface patterns across the full claim portfolio; experienced investigators interpret those patterns and build actionable cases. A carrier whose investigators engage after payment has already occurred is operating a fundamentally different system from one whose analytics flag suspicious patterns before payment is authorized. Ask your carrier which one describes them.
Detection timing relative to your exposure. The financial damage from this advanced fraud is heavily concentrated in the period between when a scheme starts operating and when it’s identified. Our analytics models are designed to detect fraud within days – and sometimes hours – of first notice of loss, not because every claim warrants that scrutiny, but because the schemes that cause the most damage are the ones that scale before they’re spotted. Ask your carrier what their typical detection lag looks like for the fraud types most common in your industry.
Industry-specific pattern recognition. Fraud patterns are not uniform across industries. Construction fraud looks different from manufacturing fraud, which looks different from transportation fraud. Medical billing fraud leaves different indicators than contractor fraud, which, in turn, presents differently than staged accidents. Generic across-the-board detection finds generic fraud. What’s accelerating is advanced, tailored fraud — schemes engineered to evade the standard triggers. Identifying those types of fraud requires deep familiarity with how fraud behaves in your specific industry, geography and vendor ecosystem. Ask your carrier what they know about fraud patterns specific to your book, not just the industry at large.
This Matters Beyond the Balance Sheet
There’s a perception — one I hear sometimes even from experienced risk professionals — that fraud is primarily an insurer’s financial problem. I want to push back on that.
In workers compensation, fraud often means injured employees are steered toward suspect medical providers and subjected to unnecessary treatments — sometimes needless surgeries — by networks built to maximize billing, not necessarily to deliver care.
Across commercial lines, fraud drives up costs that flow to employers, consumers and communities. For businesses, that translates to premium pressure, operational disruption and exposure to liability claims they’re not equipped to recognize as fraudulent until significant damage is done.
The fight against fraud isn’t self-serving for insurers. It’s about protecting the integrity of a system that businesses and individuals depend on — and making sure that when someone files a legitimate claim, the process works the way it should.
The Standard Has Changed
The question for risk managers is whether their carrier has solved the pattern problem — whether they’re monitoring claims in real time, connecting dots across the full book and engaging investigative resources before payment rather than after.
The technology exists to find these fraud patterns. The investigative expertise exists to act on these findings. The question becomes: are both technology and investigators working together from the first moment a claim enters the system — and does your organization have a clear escalation path when the patterns emerge?
In my experience, organizations that take the answer to this question seriously before a major scheme hits are in a fundamentally more advantageous position than those that start asking it after. &
