Ask a brand running warranty on a form, a spreadsheet, and an inbox whether they have a fraud problem, and the answer is almost always no. Not defensively. Genuinely. They have never seen one.
That answer and “we have no way to see one” are the same sentence in different clothes. If coverage is checked by a person looking things up, and claims arrive as email, and nothing in the process compares this claim to the last one, then a fraudulent claim looks exactly like a legitimate one. It is approved, the replacement ships, and the file closes. Nobody was wrong. Nobody was checking.
Warranty fraud is estimated at 10–15% of claims industry-wide.
Sit with the arithmetic rather than the percentage. If you process 200 claims a month and the estimate holds for you, that is 20 to 30 claims a month where something does not hold up. Multiply by whatever a replacement actually costs you, landed, and you have a number that is not in any budget line, because it was never identified as anything other than warranty cost.
The detection side of this is not encouraging either. Manual, rule-based review catches 15–25% of it. AI-assisted review catches 60–75%. Both of those numbers are about finding fraud after it has arrived, and even the better one is a coin flip with good odds rather than an answer.
Which is the thing worth understanding before you go shopping for a fraud tool: detection is the weaker half of this problem.
The word invites you to imagine somebody sitting down to defraud your brand. Some of that exists. Most of what shows up is quieter and more ordinary than that.
A customer files a claim, hears nothing for a week, and files again. Now there are two open claims on one item, and if they go to different people, you might replace it twice.
A product broke fourteen months into a twelve-month warranty. The customer remembers it as “about a year.” The person on your end has no purchase date in front of them and takes the claim in good faith. Neither party did anything wrong, and you just funded a replacement you did not owe.
A customer claims on an item they bought from a reseller who was not authorized, or on a unit they received as a gift, or on one that was registered a year ago by somebody else entirely. Each of those is a coverage question, not a character question, and without a record none of them are answerable.
That is the honest shape of the 10–15%. Some of it is deliberate. Most of it is the predictable result of a process where nobody can check anything, and the cost lands identically either way.
It is how to make most of it structurally impossible.
That distinction matters because prevention and detection have very different economics. Detection runs after the fact, costs labor every time, and by its own benchmark misses somewhere between a quarter and three quarters of what it is looking for. Prevention runs once, at the point where the record is created, and it does not get tired.
Four things do most of that work, and none of them is clever.
A registration resolves against a real order line item. You cannot register a product nobody bought, because the registration has to point at an actual purchase. Where a customer bought through retail and has no order number, the substitute is a serial number and a proof of purchase rather than nothing at all.
One registration per unit sold. The same item cannot be registered twice, because the database rejects the second attempt outright. Not flagged for review. Rejected.
One open claim per registration. Duplicate concurrent claims on the same item do not reach two different people on your team, because the second one cannot be created while the first is open.
Coverage dates locked at registration and never changed. “It broke last year” does not survive a window that was fixed on the day of purchase and cannot be edited afterward. This is the one that quietly saves the most, because date ambiguity is the most common and the least malicious leak of the four.
Read that list again and notice what is absent. No model, no score, no confidence threshold, no review queue. Four constraints on the shape of the data, and the majority of what you would otherwise have to catch never enters the system.
Plenty, and it is worth being clear about it.
Structure cannot tell you whether a defect was genuine or whether somebody put their boot through it. It cannot see the customer filing their fourth claim in six months under slightly different names, or the cluster of claims arriving right before a warranty window closes, or the pattern that only looks like a pattern across a hundred claims.
That residue is where analysis earns its place. Every claim gets classified on arrival, and anomalies get surfaced for a person to look at: duplicate patterns, suspicious timing, near-expiry clusters. It does not decide anything. It routes, and your team rules on it.
And every claim carries an immutable audit trail, which matters more than it sounds. A disputed decision with a record behind it is a conversation. The same decision with nothing behind it is your word against theirs, and you will usually fold, because the replacement is cheaper than the argument.
We are not going to tell you this eliminates warranty fraud. Anyone who does is selling you a number they invented.
Verified detection tops out at 60–75% with AI assistance against 15–25% for manual rules, and no structural constraint reaches the customer who genuinely broke the thing and says otherwise. Some fraud is going to survive any system you buy, including ours.
The true claim is narrower and stronger: most of it stops being possible. Duplicate registrations, duplicate claims, sliding coverage windows, and claims against products that were never purchased account for the bulk of the 10–15%, and all four of those stop at the schema rather than at a reviewer's judgment.
What you get from that is not a fraud program. It is a warranty system that can say no, with a reason, and show its work afterward. If the current answer to “was this covered?” is somebody's best recollection, you do not have a fraud problem yet. You have a visibility problem, and the fraud problem is downstream of it.