Claims Fraud Rings: How to Detect Organised Fraud

This article explains why file-by-file checks are structurally blind to organised fraud rings, and how cross-file connection analysis (addresses, phone numbers, professionals) brings them to light.

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Perfect files, an invisible network

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In 2024, ALFA recorded €902 million in detected insurance fraud, up 29.8% compared with 2023 [1]. Claims fraud remains at the core of the risk: fictitious, exaggerated, opportunistic claims. But the costliest category is also the least visible: organised fraud rings.

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The principle is simple: no single file looks suspicious on its own. A shared-liability collision, consistent quotes, policyholders with no prior history. Every file passes the checks.

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It is the repetition of the pattern, spread across several months, several policies, sometimes several insurers, that constitutes the fraud.

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Three forms of organised fraud

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• Fake accident rings: staged or deliberately caused collisions, collusive amicable accident reports, fictitious passengers added to multiply bodily injury claims.
• Complicit professionals: garages that inflate quotes or bill for repairs never carried out, tradespeople who certify non-existent damage, intermediaries who recycle the same supporting documents.
• Serial files: the same pattern duplicated under different identities: identical wording in statements, the same retouched photos, the same payout channels.

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Why your current checks don't catch them

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• Checks are unitary: business rules and case-handler reviews assess one file at a time, not a population of files. Yet the network only exists in the links between files.
• Identities change, infrastructure stays: the fraudster varies names and policies but reuses addresses, phone numbers and IBANs. It's these constants that give them away.
• Detection comes after payment: for every fraud detected, 3 to 5 slip through. Hidden fraud costs up to 5 times more, because it takes root and repeats.

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The solution: cross-file multi-data matching

The 4 connections to monitor

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• Contact-detail connections: an address, phone number or email shared between "unrelated" policyholders; recently created contact details with no verifiable web history.
• Financial connections: the same beneficiary IBAN recurring across several payouts; neobank accounts never previously used on the policies concerned.
• Professional connections: the same garage, assessor or tradesperson present in an abnormal proportion of files; a professional whose business shows unfavourable KYB signals (recently created, litigation history, high-risk directors).
• Behavioural connections: statements filed from the same device or IP address, identical wording, claims filed shortly after taking out the policy.

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The Meelo approach

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Meelo cross-references more than 400 signals across three dimensions: identity (who is filing?), behaviour (how is the file submitted?) and funding (which account does the money go to?).

Multi-data matching automatically checks every new claim against your history: addresses, IBANs, phone numbers, devices, professionals involved.

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A file that looks impeccable on its own stands out as soon as it shares constants with others. The decision comes back in 2 to 5 seconds, with no friction for honest policyholders, and connected files are escalated for review along with a map of the network.

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3 immediate actions

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• Index the constants: build a cross-file register of the addresses, IBANs, phone numbers and professionals present in your claims files, and check every new file against it.
• Run KYB checks on professionals: recurring garages, tradespeople and assessors should be verified as partners (entity, directors, litigation history, real activity), not just as suppliers.
• Score the policy-to-claim link: a claim filed shortly after a policy is taken out, on a policy sold with recently created contact details, always deserves an enhanced review.

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Key takeaway

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Organised fraud doesn't hide inside files: it hides between files. As long as checks stay unitary, the networks stay invisible, and they are the costliest patterns on the market. Cross-file multi-data matching turns every paid file into a signal for the next one.

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Discover Meelo: request a demo on your claims use cases, or test it on a sample of your 2025 files. No heavy integration, measurable results within days.

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Sources

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• ALFA / Itesoft & LeLynx.fr (September 2024). Insurance fraud report: €902 million detected (+29.8% vs 2023).
• Abeille Assurances (April 2025). Estimated total fraud: €2.5 billion, or 5% of premiums; €105/household/year.
• Meelo (2026). Insurance fraud use cases: organised claims typologies and network signals.

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