A well-known European insurer was losing money to sophisticated claims fraud — while its own honest customers waited days for a yes. We built a real-time risk engine that pays the clean claims in seconds and points a human at only the ones that smell wrong.
A traditional financial institution was losing real money to people exploiting gaps in its claims and credit-approval processes — forged documents, false statements — while the fix it had put in place made everything slower for everyone else.
The control teams were drowning: underwriters and reviewers went through thousands of documents, payslips and reports by hand every day, and the manual check became a monstrous bottleneck that held up approvals for good, legitimate customers. Meanwhile the old rules-based system flagged anything slightly unusual as a risk (false positives), blocking innocent customers and burning service-rep hours on releasing the blocks. Fraud slipped through; honest people waited.
Fast where it's safe, human where it counts — with the reason for every flag spelled out.
We connected an AI model that scans every incoming request or claim in seconds and cross-references it against the account's own history, behavioural patterns and external data sources — a live risk read instead of a rigid rule.
Innocent requests that clear a high certainty bar (above 95%) are approved and paid automatically within seconds, with no human touch — the honest customer stops waiting behind everyone else's suspicion.
Suspicious cases are flagged immediately and routed to a human investigator — together with a "red summary" that spells out exactly which document looks forged or which contradiction in the data triggered the flag.
By scoring on real signals rather than blanket rules, the flood of false-positive alerts that used to block innocent customers dried up — investigators stopped chasing ghosts.
No black-box "denied". Each escalation arrives with the evidence behind it, so an investigator starts from a lead, not a blank page — and a decision can be defended.
The engine runs inside the actual claims and approval flow against real data, so it works on the messy Tuesday-afternoon caseload — not a curated demo of well-behaved claims.
The two goals that used to fight each other — catch more fraud, approve honest customers faster — started moving together.
Losses paid out on fraudulent claims and requests fell by 60%, and the time to approve a legitimate customer dropped from five working days to thirty seconds — a step-change in customer satisfaction, not just cost.
Four in five of the old "false alarm" alerts were eliminated, freeing the control team to focus only on genuine risk instead of unblocking innocent customers all day.
What makes it safe to run at speed is the split: the model only auto-approves where it's near-certain and the mistake is cheap, and every escalation carries the evidence a human needs to decide and to defend. Automation on the clear cases, a person on the doubtful ones, a reason attached to both — the same discipline behind our data-readiness work in the same industry.
Reef TRH