Detect fraud-signal patterns in claims narratives
Some fraudulent claims read differently from honest ones — not in the numbers, but in how the story is told. NEXT reads the free-text narrative on a claim and finds the wording patterns that tend to show up in cases worth a second look. The fraud team gets a routed case with the specific phrases, comparable past claims, and a short note on why it was surfaced — ready to investigate.
This does not decide fraud. It decides where a trained investigator should spend the next hour.
What the routed case looks like
Example output based on grouped claims narratives and prior investigated cases. Numbers are illustrative.
Claim
Household contents, single-incident theft, mid-value
What the narrative shows
A pattern that recurs in previously confirmed cases: high-value items described in round numbers, all purchased recently, with documentation said to be lost in the same incident.
Representative phrasing from the narrative
"I can't find any of the receipts — they were in the same bag that got taken."
"Everything in the room had been replaced in the last month, all top of the range."
Comparable past cases
14 claims this quarter share the same narrative shape; 5 of the closest matches were referred to investigation last year, and 3 were ultimately declined or withdrawn.
Commercial exposure
The matching claims represent roughly £180K in requested payout.
Signal strength
Strong on the missing-documentation and recent-purchase pattern; mixed on item valuation, which is plausible for the policyholder's profile.
Caveat
Narrative signal is one input. Several of these claims will be entirely legitimate — recent renters and new homeowners genuinely do have new, undocumented contents. The case is routed for review, not for denial.
The investigator opens the case and the pattern, the phrasing, and the comparable history are already attached.
How NEXT detects this
NEXT reads the qualitative parts of a claim — the first-notice-of-loss description, adjuster notes, and any written policyholder statements — alongside how those narratives compared to claims that were later investigated or declined. It keeps a continuously updated record of which narrative shapes correlate with elevated fraud risk for each claim type. When a new claim's wording matches one of those patterns above a set threshold, NEXT routes the case to the fraud team and writes in the specific phrases, the comparable past claims, and why it was surfaced. The investigator decides whether to open a full inquiry. NEXT supplies the qualitative signal; the referral decision stays with the team.
Why fraud surfaces late today
Most fraud detection runs on structured data — claim amount, frequency, location, time since policy inception. The narrative, where a lot of the early signal actually lives, gets read once by a busy adjuster and then summarized into a few structured fields. The original wording is gone by the time anyone with fraud training sees the file.
The tools meant to catch this wait to be used. A rules engine fires on keywords, produces a long list of mostly-cleared flags, and reports what already happened — it doesn't tell you which case is worth your morning. Ask an AI assistant and you get the loudest recent match, not the pattern across the quarter. Neither comes looking for you.
So detection depends on which adjuster handled the file and whether they happened to notice. One reviewer escalates a story; another reads the same words and clears it. That inconsistency is the real cost — not just missed fraud, but a referral process that can't be explained or repeated.
A dashboard still waits for someone to notice the pattern. NEXT brings the matched cases to the fraud team and attaches the wording it matched on.
How this compares to the tools you already know
Approach | Where the evidence lives | What the fraud team does at decision time |
|---|---|---|
Manual SIU referral | In adjusters' heads and scattered file notes | Reads the file cold and reconstructs why it was flagged |
Rules / keyword flags | In a static rules list | Works through high-volume, low-precision flags and clears most |
AI assistant you query | Wherever you think to ask | Has to know what to ask; gets the loudest single match |
NEXT | Written into the routed case with the narrative excerpts and comparables | Starts from the pattern and the history, decides whether to investigate |
What changes for the fraud reviewer
Today you either trust the adjuster's referral or you sample files and hope. When a case lands on your desk, you read it cold — you reconstruct what the policyholder actually wrote, pull comparable claims from memory, and decide whether the story holds. That archaeology is the job, and it doesn't scale.
With NEXT, the case arrives with the work already done. The phrasing that triggered it is quoted in front of you. The comparable past claims — including how they resolved — are listed. The claim that looked routine reads differently once you see fourteen others with the same shape sitting behind it.
The scenario that repeats: a contents claim clears the adjuster because nothing in the numbers stands out. NEXT routes it because the narrative matches a pattern that resolved badly five times before. You open an inquiry you would never have known to start. You still make the call — NEXT decides where you look, not what you conclude.
Downstream effects
Consistency across reviewers. The same narrative pattern gets surfaced the same way regardless of which adjuster handled intake — which is the operational consistency the team is actually after.
SIU capacity goes where it pays. Investigators spend less time reconstructing files and triaging keyword noise, and more time on cases with real qualitative signal behind them.
A defensible audit trail. Every referral carries the specific wording and comparables it was based on, so the decision to investigate can be explained to compliance, reinsurers, or a regulator.
Where the human stays in control
NEXT does not decline claims, deny payouts, or label a policyholder as fraudulent. It routes cases and attaches the qualitative signal. You set the threshold for what gets surfaced, and you can require that matches are reviewed before anything is written into a case. Tuning that threshold and the comparison set is configuration work, not approval work — once it reflects your risk appetite, the routing runs, and the investigation decision stays entirely with trained staff.
What to configure first
The detection is only as good as the history behind it. Before turning this on, make sure NEXT can read enough resolved cases — both confirmed-fraud and confirmed-legitimate — so the patterns reflect your book, not a generic model. Decide which claim types to start with; narrative signal is stronger in some lines than others. Set the routing threshold deliberately: too loose and you recreate the false-positive problem, too tight and you miss the marginal cases that matter most. Agree where review happens and who owns the referral decision. And confirm the narrative sources are actually captured in text — if intake is mostly phone with thin notes, coverage will be patchy until that improves.
Where this breaks down
Thin or missing narratives
If claims are taken by phone and logged as a few structured fields, there is little text to read. NEXT can only detect patterns in language that was actually captured.
Over-tight thresholds
Set the bar too high to keep referral volume down and you suppress the borderline cases that are exactly where review adds value. Calibrate against resolved outcomes, not against how many alerts feel comfortable.
Treating signal as a verdict
The narrative pattern indicates risk, not guilt. Honest policyholders — new renters, recent buyers, people who genuinely lost documentation — will match. If reviewers treat routing as a conclusion rather than a starting point, you trade missed fraud for wrongful denials.
Stale comparison history
Fraud tactics shift. If the resolved-case history NEXT learns from is never refreshed, the patterns drift toward last year's behavior. Keep feeding investigated outcomes back in.
FAQ
How is this different from our fraud rules engine?
A rules engine matches structured fields and keywords against a fixed list, which is why it produces so many low-value flags. NEXT reads the narrative as language — the shape of the story, not just the presence of a word — and weights it against how similar narratives actually resolved. It also routes the matched case with the specific phrasing attached, so the reviewer starts from evidence rather than a bare alert.
Does NEXT decide which claims are fraudulent?
No. NEXT detects narrative patterns associated with elevated risk and routes those cases to the fraud team for human investigation. It never declines a claim, denies a payout, or labels anyone. Every referral and every decision about it stays with trained staff.
Won't this just create more false positives for the SIU team?
It can if the threshold is set carelessly. The point of calibrating against resolved outcomes is to surface fewer, better cases than a keyword engine does. You control how aggressive the routing is, and you can require human review before anything is written into a case.
What claims data does NEXT read?
The qualitative parts of the claim: first-notice-of-loss descriptions, written policyholder statements, and adjuster notes, alongside the resolved history of prior investigated and declined cases. The richer and more text-based your intake, the stronger the signal. Phone-only intake with thin notes limits coverage.
Can adjusters see why a case was routed?
Yes. Each routed case carries the specific phrasing it matched on, the comparable past claims, and a short note on why it was surfaced. That transparency is the point — it makes referrals consistent and gives you an audit trail you can show compliance or a regulator.
What if fraudsters learn the patterns and change their wording?
Patterns do shift, which is why the comparison history has to stay current. As newly investigated outcomes are fed back in, the narrative shapes NEXT detects move with them. Detection that learns from your resolved cases adapts faster than a static rules list that someone has to rewrite by hand.