If you were to ask 100 insurance professionals, “What is insurance fraud?” I’d wager 99 of the responses would center around claims fraud. Now if you were to ask a follow-up question, “When do you start thinking about claims fraud?” I’d wager a similar number would answer with some variation of, “When the claim is filed.”
Which makes sense, as claims fraud is typically something you think about *after* a claim is filed.
But what if I told you that signs of claims fraud exist long *before* a claim is filed?
It’s true. The behavioral signals that distinguish a legitimate claimant from someone planning to manipulate a payout exist long before FNOL. They’re there at the quote. They’re there when the person logs into the policy admin system in the weeks before filing. They’re there when the person starts filling out the FNOL intake. The fraud doesn’t *start* at the claim. The claim is just where it becomes expensive enough to notice, and by then, you’re incurring costs whether you deny the claim or not.
We bucket the claims detection process into four categories:
- Pre-Bind (Application)
- Post-Bind (Policy Admin)
- FNOL (Claims Intake)
- Post-Claim (SIU)
This is the gap I want to walk through here, because I think most of the conversation around insurance claim fraud detection is focused on the wrong part of the timeline.
The Problem With Starting at FNOL
The standard model for fraud detection in insurance claims is reactive: you wait for a claim to be filed, apply your detection tools, and decide whether to route it to SIU. It’s a triage model. And for most carriers, it’s the only model they have at the claims stage.
The problem isn’t that FNOL triage doesn’t work. It does work, and it’s a necessary part of the process. The problem is that by the time a claimant is in your intake form, they’ve already made a series of decisions. They’ve already chosen the claim amount they’re going to push for. They’ve already decided whether they’re going to stay on the digital channel or try to take the interaction offline. They may have already changed their bank account details in anticipation of where they want the payment to land.
The fraud is already in motion. Your detection tools are joining the conversation mid-sentence.
Not only that, but traditional claims fraud tools are constantly looking in the rearview mirror, trying to piece together what happened. What they can’t see is how it happened – the behavioral sequence that preceded the suspicious transaction.
A legitimate policyholder updating their bank account moves through that process differently than someone who just took over the account. A genuine claimant filling out an FNOL form moves through it differently than someone who’s been studying your payout structure and reverse-engineering the intake questions. The behavior is different even if the data they enter is identical.
Stage 1: Pre-Bind — The Signal Is There Before the Policy Exists
This is the part of the conversation that surprises most claims and SIU leaders the first time they hear it.
The behavioral patterns that are predictive of future claims activity often show up during the quoting process, before the policy binds, and before there’s anything to investigate.
Today, most of our customers are in growth mode and leveraging our Conversion Score to predict an applicant’s likelihood to purchase in real-time to drive more conversions, improve monetization, save on 3rd party costs for low-intent users, and more. That said, we continue to stress that looking at purchase intent only gives you a one-dimensional view of the applicant. Is the applicant super high intent because they purchased a car this morning or because they accidentally backed into their garage this morning and don’t have the proper coverage?
Focusing solely on purchase intent unintentionally opens you up to tons of bad risk. The applicants may look highly motivated and great on paper, but ultimately will hurt your loss ratio when they file an early claim and churn shortly after. Good news is their behavior tells a totally different story than the final answers they submit.
The person who already knows they’re going to file shops differently. They navigate coverage selections with more deliberateness. They re-quote after entering specific information. They spend time in fields that someone buying insurance for the first time wouldn’t linger on. The behavioral fingerprint of a claims-intent applicant is distinct, and it’s there at the application, not just at FNOL.
We work with a majority of the top 10 P&C carriers. The teams doing the most sophisticated work on insurance claim fraud detection are starting to use pre-bind behavioral signals exactly this way, as a claims-propensity flag that routes certain policies for closer underwriting scrutiny before they ever bind. Not as a reason to deny coverage, but as a signal that this policy warrants a higher level of review upfront and then, if and when a claim comes in, will be prioritized for review.
We also have customers adding intentional friction to the application process for those most likely to file an early claim, such as routing them to an agent or call center or requiring a bigger partial payment or pay-in-full to intentionally weed out bad risks. If you knew an applicant was 400% more likely to file a claim within the first 30 days, for instance, you’d likely want to do something about it, wouldn’t you?
Stage 2: The Policy Admin System — Where Bad Actors Set the Stage
Here’s the part of the policy lifecycle that gets the least attention, and it’s arguably where the most preventable loss happens.
Before a fraudulent claim is filed, the bad actor usually has work to do. They need to redirect the payout, so they change the bank account. They need to make sure communications go to them and not the actual policyholder, so they update the email or phone number. They need the claim to process smoothly, so they manipulate the profile, sometimes creating multiple account IDs to obscure the trail.
All of this happens inside the policy admin system. And all of it is behaviorally anomalous.
A legitimate policyholder updating their bank details moves through that process differently than someone who just took over the account or is preparing the ground for a fraudulent filing. The session pace is different. The navigation pattern is different. The sequence of what gets changed, and in what order, is different. Robotic, paste-heavy, methodical edits to high-value fields like payment destinations and contact details look nothing like how a real policyholder manages their account.
But here’s the thing that makes the policy admin system the most important blind spot in the entire detection model: many bad actors who do their setup work here then file the claim off-channel, by phone, by paper, or by fax. They deliberately avoid the digital claims intake because they know it’s being watched.
We’re seeing this pattern across carriers we work with. The behavioral fingerprint in the policy admin system is clear, high-risk changes to bank account and communication preferences, mismatched timezone and device activity suggesting foreign-based access, single devices associated with multiple profile IDs that no legitimate user would have reason to create. By the time the claim comes in through a call center or a paper submission, there’s no digital signal at the claims stage. The only window that existed was inside the policy admin system, and if you weren’t watching it, you missed it entirely.
By the way, the intervention doesn’t have to be a hard stop. Carriers we work with are exploring a range of responses calibrated to the risk level: requiring step-up authentication when unusual account changes are detected, routing the session to a contact center, disabling the ability to modify banking information until after a claim has been submitted, or simply surfacing the behavioral risk indicators to a call center representative in real time if the user calls in. The goal is to convert the behavioral signal into an operational workflow, not just a data point in a report that nobody reads until after a payout has already processed.
The policy admin system, in other words, is often where the fraud case is made or missed. Detection at FNOL is valuable. But if the setup already happened inside the account and the claim came in on paper, digital FNOL detection never got its turn.
Stage 3: FNOL / Claims Intake – Triage at the Moment of Filing
This is where most of the fraud detection tools for insurance claims processing are focused. Real-time detection at FNOL is where the most immediate loss avoidance happens.
The way behavioral detection works at FNOL is simple: as a claimant moves through the intake form, we are watching how they interact with it. Not what they enter, but how. Answer manipulations. Paste-heavy entry into specific fields. Unnatural navigation that suggests the person knows exactly what they’re looking for. Field edits that follow a pattern consistent with payout maximization, or payouts just below the “automatic review” threshold.
These behavioral signals fire in real time, before the claim is submitted, before the payout processes. Suspicious sessions get flagged and routed to SIU with context and a behavioral record already attached. What fields, what edits, how this session compared to the behavioral baseline for legitimate claimants at this stage.
And arguably most importantly, the sessions that don’t trigger those signals move straight through. No additional friction, no unnecessary review, no slowing down the legitimate policyholder who’s already having a bad day.
This is the part of the detection model I find carriers get most excited about because the ROI on a stopped payout is immediate and measurable. But I’d push back on stopping the conversation there, because the value of post-bind detection is significantly higher when it’s connected to what came before it.
A behavioral flag at FNOL on a policy that also had elevated behavioral risk signals at the original application is a much stronger case than a behavioral flag at FNOL in isolation. The pattern is corroborated. The intent is harder to argue against. SIU has a thread to pull, not just a data point.
By the way, the carriers we work with consistently tell us that the referral quality problem is as significant as the detection gap itself. SIU teams that receive high-volume, low-context alert queues spend meaningful time on cases that don’t hold up under scrutiny. Behavioral context attached to each referral changes that calculation substantially.
Stage 4: Post-Claim — Giving SIU the Behavioral Record to Build the Case
Here’s the part of the insurance claim fraud detection conversation that gets the least attention and arguably delivers some of the highest long-term value.
Catching fraud in real time is the goal. But fraud doesn’t always get caught in real time. Claims pay out. Policies get rescinded after the fact. Lawsuits get filed. Regulatory bodies come calling for lookback records. The question then isn’t “How do we flag this faster?” It’s “Can we reconstruct what actually happened?”
If the behavioral data is captured and stored from day one, SIU investigators have a complete, searchable record to work from. Every session. Every screen visited. Every field edited. Every high-risk signal that fired, where, and when. From the original application through the claim filing and through every account interaction in between.
This is what I mean when I talk about a behavioral record as an evidentiary asset, not just a detection tool. A session timeline that starts at the first quote screen, continues through bind, policy adjustments, FNOL, and continues on through the full claims flow gives investigators the full narrative of intent. This is part of our broader Enterprise Intent Solution, which you can read more about here.
Our new Global Search functionality, in the context of how we think about SIU tooling, means investigators can pull any session, by policy ID, quote number, or behavioral anomaly, from anywhere in the workflow. In seconds, not days. The Session Timeline renders the full chronology: which screens, how long, what changed, when the risk signals appeared, from where, and more.
That shift, from digging through disconnected systems to having a queryable behavioral record available instantly, helps speed up review times, leading to quicker payouts for genuine claims while saving on claims payouts for illegitimate ones.
What About “AI” Insurance Claim Fraud Detection?
The conversation around AI for insurance claim fraud detection tends to run toward one of two failure modes: overselling it as the answer to every fraud problem or dismissing it as hype.
The honest version is more specific. AI and machine learning add real value in many ways, one of which is the context of a behavioral detection system because they can identify the patterns within patterns, the behavioral combinations that correlate with fraud outcomes, that rule-based systems miss by design.
We’re also seeing some really cool stuff in the AI voice detection space, in DeepFake identification technology, in AI fraud tools that validate photos and submitted documents, and more. As fraudsters use more and more AI, it will come down to fighting fire with fire.
A rule-based system catches known fraud. A model trained on behavioral outcomes, i.e., what the sessions that preceded fraudulent claims actually looked like, at scale, across millions of interactions, catches what no rule was written for yet. The novel scheme. The first-time fraudster. The pattern that’s new enough that no one has codified it into a detection rule.
The combination is what works: rules for known patterns, models for emergent ones, and a behavioral data layer that gives both something meaningful to operate on.
Bottom Line
I’ll leave this with the framing that I think changes how most carriers approach this problem when they sit down to think about it.
Insurance claims fraud isn’t just a claims problem. It’s a lifecycle visibility problem.
The same bad actor who filed a fraudulent claim in month six was probably showing behavioral signals at the original application. They may have been active in the policy admin system in the weeks before filing. The fraud touches multiple systems, multiple teams, and multiple points in time, but the behavioral fingerprint is continuous.
Most carriers are trying to detect it in one stage: usually at FNOL, sometimes in the policy admin system, rarely at the application. The detection tools that exist for each stage are siloed because the teams that own each stage are siloed. Claims doesn’t see what underwriting saw. SIU doesn’t see what happened at the original quote.
The carriers making the most progress on insurance claim fraud detection are the ones connecting those stages with a single behavioral layer, one that accumulates evidence from the first session, flags risk in real time at every subsequent touchpoint, and hands investigators a complete record when a case needs to be built.
The fraud signal is rarely missing. More often, it’s present but invisible to a system that only started looking when the claim came in.
Interested in what this looks like across a real carrier deployment? Let’s talk.
Recommended Articles for You:
→ Insurance Fraud Detection: The Complete Guide for Carriers
→ Hard Fraud in Insurance: Bots, Ghost Brokers & Fraud Rings
→ How Behavioral Analytics Detects Insurance Fraud
→ Real-Time Fraud Detection: Why the Detection Window Is the Application
