A single suspicious detail on a claim rarely proves fraud on its own. An adjuster who acts on one red flag alone risks denying a legitimate claim or missing the pattern that points to a real problem.

The harder question is what to do when several small inconsistencies show up at once, and how to weigh evidence like doorbell or ring camera footage when it becomes part of the file.

This article covers how to categorize fraud red flags, how to evaluate camera footage as evidence, and where litigation risk fits into a claims workflow that keeps a human decision-maker in charge.

What Counts as a Fraud Red Flag

Red flags are not proof of fraud. They are signals that tell an adjuster or special investigations unit (SIU) analyst to look closer at a claim before approving it.

Industry practice generally holds that no single indicator justifies denying a claim. Investigators look for a combination, often three or more, before escalating to a full investigation by the SIU.

That threshold exists for a reason. Life circumstances that look suspicious in isolation, a recent move, a vague description of an item, a claimant who is hard to reach, are common and usually innocent on their own.

The Case for Multiple Indicators

Acting on one red flag alone creates real risk. A claimant who recently moved, or who is knowledgeable about claims terminology, is not automatically committing fraud.

Adjusters with claims experience often develop pattern recognition without a documented process, and that instinct is valuable. But instinct alone is hard to defend if a denied claim is later challenged.

The strength of a red-flag process comes from pattern recognition across categories, not from treating any one signal as disqualifying. Documenting which combination of flags triggered an escalation protects the decision, whichever way it goes.

A Three-Category Framework

Grouping red flags by category makes them easier to apply consistently across adjusters. It also gives a claims team a shared vocabulary when a file gets handed off or reviewed by a supervisor.

None of these categories operates in isolation during a real investigation. A claim filed shortly after a policy starts, paired with vague incident details and a gap in the supporting footage, spans all three categories at once. That overlap is usually what triggers a closer look, not any single line item on the list.

Applying the Framework Without Slowing Down Legitimate Claims

A red-flag framework only works if it speeds up the review of clean claims as much as it slows down suspicious ones. If every claim gets the same level of scrutiny, the SIU becomes a bottleneck and honest policyholders wait longer than they should.

Most teams handle this by using the framework as a triage step, not a full investigation trigger. A claim with zero or one flag moves through the normal process. Two or more flags across categories routes to a documented secondary review before a final decision.

Ring and Doorbell Camera Footage as Evidence

Smart doorbells and ring cameras have become a common source of claim evidence, and insurers generally accept the footage when it is handled correctly.

The growth of these devices means claims teams are seeing more video evidence than they did even a few years ago, often submitted directly by the policyholder rather than collected by an adjuster in the field.

What Makes Footage Usable

Insurers expect footage submitted unedited, saved promptly after the incident, and accompanied by a clear account of when and how it was captured.

Cropping, filtering, or otherwise altering a clip before submission is a mistake even when the intent is innocent. Any edit can itself become a red flag, since it introduces a question about what the original footage showed.

Claimants are generally better served by submitting the full, unedited clip and letting the claims team ask for a specific segment, rather than pre-selecting what they think is relevant.

What Raises Suspicion

Claimants sometimes lose footage because storage is subscription-based and clips expire after a set window. That is a legitimate limitation, not fraud, but it does affect what an adjuster can verify.

Footage that starts just after an incident, or that has visible gaps, warrants the same scrutiny as any other evidence-related red flag from the framework above. The absence of footage is not itself suspicious. A convenient or repeated pattern of absence is.

Chain of Custody Matters More Than People Expect

Video evidence carries more weight when the claims team can establish who captured it, when, and whether it has passed through any editing software since. This is true whether the footage supports the claim or raises questions about it.

A simple practice that helps on both sides: ask the claimant to note the device model, the approximate timestamp of the incident, and whether the footage was downloaded or shared directly from the manufacturer's app. That detail is far easier to gather at intake than to reconstruct later.

Where Litigation Risk Fits In

Litigation risk is not a single-factor call any more than fraud is. It is a pattern read across a claim's history and characteristics.

Reading the Pattern Early

Claims that combine multiple red flags, contested evidence, and an uncooperative claimant tend to carry higher litigation risk than any one factor would suggest alone.

Prior claims history matters here too. A claimant with a pattern of disputed settlements, even across different insurers, is a data point that's easy to miss without a system built to surface it.

Flagging that pattern early lets a claims team intervene proactively, rather than responding after a dispute has already escalated.

Why Early Flagging Changes the Outcome

A claim reviewed for litigation risk at intake can be routed to a more experienced adjuster or documented more thoroughly from the start.

That is different from catching risk only after a claimant has retained counsel, when the options for resolution narrow considerably. At that stage, the cost of resolving a dispute typically includes legal fees on both sides, not just the underlying claim amount.

Litigation Risk Is Not the Same as Fraud Risk

It is worth separating these two concepts clearly, since they overlap but are not identical. A claim can carry high litigation risk without any fraud indicators present at all, for example a complex bodily injury claim with a cooperative but frustrated claimant.

Conversely, a claim with several fraud red flags may resolve quietly through the SIU process without ever approaching litigation. Treating the two as interchangeable risks either over-escalating routine disputes or under-resourcing genuinely high-risk fraud cases.

Building a Red-Flag Workflow That Doesn't Over-Rely on One Signal

Why the Threshold Matters Operationally

Treating any single red flag as automatic grounds for denial exposes an insurer to bad-faith claims and reputational damage. It also creates inconsistency across adjusters, since one person's threshold for suspicion rarely matches another's.

A documented threshold, such as requiring multiple corroborating indicators before escalation, protects both the claimant and the insurer's decision-making record. If a denial is ever challenged, a documented, consistently applied process is far easier to defend than an individual adjuster's judgment call.

Keeping the Human Central to the Call

Red-flag frameworks and evidence checklists are tools for a person to use, not substitutes for that person's judgment.

The adjuster or SIU analyst still makes the final call on whether a claim moves to investigation, based on the full file rather than any one indicator. Tools that surface patterns are most useful when they save a reviewer time finding the pattern, not when they attempt to make the decision on their own.

Training New Adjusters on the Framework

A category-based framework is also easier to teach than a long, undifferentiated list of red flags. New adjusters can learn the three categories quickly and build judgment about combinations over time, rather than memorizing dozens of individual indicators with no organizing structure.

Pairing the framework with a handful of real, anonymized examples from a team's own claim history tends to accelerate this faster than generic training material.

How InsOps Helps

InsOps builds an insurance-trained AI that assists claims teams in surfacing high-risk claims earlier. LiLa, our insurance-trained LLM, reduces litigation risk by analyzing case patterns and claim history to surface high-risk claims.

LiLa runs inside your own environment, so PII and PHI never leave controlled infrastructure. A person reviews and validates every flagged claim before any action is taken.

Our Integration Gateway connects to Guidewire ClaimCenter, so case history and claim data flow directly into your existing workflow without custom engineering.

InsOps is also building toward image-based fraud detection inside LiLa, to help evaluate photo and video evidence alongside the red-flag categories covered above. If you are evaluating how to catch litigation risk earlier without adding headcount to your SIU, contact us to talk through what this could look like for your operation.

Frequently Asked Questions

What is a fraud red flag in an insurance claim?

A fraud red flag is a specific detail in a claim, such as timing, inconsistent documentation, or unusual claimant behavior, that signals a claim may need closer review before approval.

Why does fraud detection matter for claims teams and policyholders?

Unchecked fraud raises premiums for honest policyholders and slows down claims processing generally, since every claim needs enough scrutiny to separate legitimate cases from suspicious ones.

How should an adjuster evaluate ring or doorbell camera footage?

Treat unedited footage submitted promptly, with a clear account of when it was captured, as stronger evidence. Edited clips or unexplained gaps in footage should be evaluated alongside other red flags, not in isolation.

What's a common challenge in acting on red flags?

Adjusters can either under-react, missing a pattern across a file, or over-react to a single indicator and risk a bad-faith denial. A documented multi-indicator threshold helps avoid both.

What metrics show a fraud-detection process is working?

Useful measures include the rate of claims escalated to SIU that are later confirmed as fraudulent, time to escalation, and how often litigation follows claims that were not flagged early.

How does AI assist with litigation risk and fraud red flags?

LiLa reviews claim history and case patterns to flag which claims carry elevated litigation potential, so a person can prioritize those for closer review instead of relying on any single red flag.

What's a realistic timeline for building out a red-flag workflow?

Most teams can formalize a category-based red-flag framework and documentation threshold within a few weeks. Integrating it with existing claims systems and case history typically takes longer and depends on what's already in place.