Before any payout is approved, every claim runs through an automated screen for duplicate submissions, mismatched details and anomaly patterns. Flagged cases land in a special review queue with an AI-written rationale; clean claims move to payment faster than before.
Typical results
100%
Claims screened pre-payout
-1.5 days
Clean-claim payout time
7%
Claims flagged for review
Figures reflect typical results for this kind of automation.
The challenge
Fraud checks depended on whichever adjuster happened to handle the file. With hundreds of claims a month, nobody could cross-reference every submission against past claims, spot recycled invoices, or notice that the same bank account appeared on three unrelated files. Suspicious payouts slipped through, and honest claimants waited while adjusters double-checked everything manually.
Before and after
Suspicious claims caught monthly
5×Clean-claim payout time
−23%Claims screened for fraud
6.7×The automation
We inserted an automated screening stage between claim approval and payout. Every claim is checked against the full claims history for duplicate invoices, reused documents, repeated bank accounts and mismatches between the reported incident and the submitted evidence, while an anomaly model scores unusual patterns in amounts and timing. Clean claims pass through untouched. Flagged claims route to a dedicated review queue, where an AI-written rationale explains exactly which signals fired and why.
How it works
- 01
Claim approved for payout
Before any money moves, the claim enters the automated screening stage.
- 02
Cross-reference everything
The claim is checked against the full history for duplicate invoices, reused documents and repeated bank accounts.
- 03
Anomaly scoring
A model scores amounts, timing and patterns against the whole book for anything that doesn't fit.
- 04
Flag with rationale
Suspicious claims route to a dedicated review queue with an AI-written explanation of exactly which signals fired.
- 05
Clean claims fly
Everything else proceeds to payment without delay — faster than before the screen existed.
Starts from
- Approved claims
- Claims history
Delivers
- Review queue
- AI-written rationale
- Clean claims paid
The impact
A screen like this typically catches five times as many suspicious claims as manual spot checks, and confirmed fraud is stopped before any money moves. Clean claims pay out 1.5 days faster because adjusters stop second-guessing them, and every flag arrives with a written rationale reviewers can act on immediately.
Questions about this automation
What signals does the fraud screen look for?
Each claim is compared with the full claims history for duplicate invoices, reused documents, bank accounts that appear on unrelated files and mismatches between the reported incident and the evidence. An anomaly model also scores amounts and timing against the rest of the book, so unusual patterns stand out.
Does the AI decide to reject a claim?
No. The screen only flags. Flagged claims move to a review queue with an AI-written rationale listing which signals fired, and an adjuster decides what happens next. Clean claims continue to payment without extra steps, so honest claimants are not held up by manual double checks.
Where does the screening step sit in the claims process?
It runs between claim approval and payout, reading from the claims system and its history. The thresholds for what counts as suspicious are set with the claims team and tuned as reviewers confirm or clear flags. It is a multi-step build, usually rolled out in stages, starting with the duplicate and bank-account checks.