--- title: Insurance Fraud Detection and Claim Ring Analysis url: https://nexyron.tech/industries/insurance-fraud description: Find organised fraud rings hidden in your own claims book: connected claims, shared providers and adjusters, and proof of what your crackdown prevented. --- # Every claim passed. The ring did not *Insurance fraud* A score judges one claim at a time. Rings are built so that every claim survives that test. Nexyron judges the structure the claims form. ## Your scoring engine answers the only question it can represent It rates one claim against a model of claims, and after twenty years of investment it does that well. The rating is a number about a file. A file has no representation for a relationship. Fifty claims over eighteen months, each unremarkable. A thousand slightly generous treatments. Two hundred ordinary files sharing three adjusters and one hire company. Every one of them scores clean, correctly, because every one of them is clean on its own. > The ring is already in your own claims file. It has never been in one place. The industry databases answer a third question well: has this party appeared at another carrier. That infrastructure is real and it sees what you cannot see. It does not see your book. Nobody outside your organisation holds your book, and inside most carriers nobody can reach it either, because the claim, the policy, the provider, the adjuster and the note explaining what happened sit in five systems with five keys. “Is this claim suspicious” and “are these claims connected” are different questions. Asking the first one more accurately never reaches the second. ## One model of your whole book of claims, then ask it structural questions Nexyron takes the exports you already produce, claims, policies, claimants, vehicles, providers, repair shops, adjusters, addresses and accounts, along with the adjusters' written notes and the documents attached to the files, and derives one connected model from them. Not a commissioned ontology. Not a two-year data programme with an integration team. The model is derived from your records and your written knowledge, then corrected by an operator who knows the business. On that model, 63 graph algorithms are part of the query surface rather than a separate product: community detection, centrality, flow, shortest path, shared-neighbour weighting. Meaning-based search reads the adjuster notes beside the records they describe, in the same query. Your score keeps scoring. Nexyron asks the question a score has no way to hold. ## A simulated investigation Constructed to show the method. No customer, no benchmark, no performance claim. 1. **The tip** An anonymous tip names one clinic. Sixty-three claims, two years, all paid, none scored above threshold. Read by hand it takes two analysts a fortnight and answers only the question the tip already asked. 2. **The book as one network** Nexyron runs community detection across the whole book. One community holds the named clinic, two clinics that were not in the tip, a hire company, a solicitor, 41 claimants and 112 claims. The tip found a third of it. 3. **Not a coincidence** Shared attributes weighted by how surprising they are. A shared city counts for nothing. Fourteen claimant pairs share an attribute occurring in fewer than one in twenty thousand cases. Two share a bank account. Three share an address none of them lists as their own. 4. **The party holding it together** Not the clinic. A hire company appearing in 71 of the 112 claims. Remove it and the group breaks into four clusters sharing nothing. It was never flagged, because hire is routine and no single claim looked wrong. 5. **What your own people already wrote** Three adjusters remarked on the same recovery firm across unrelated incidents, nineteen months ago. All three observations were recorded. There has never been anywhere for three separate observations to become one. 6. **Did last year's crackdown work** Seven providers were delisted. Nexyron estimates the effect for four, where comparable providers exist, and states plainly that it cannot for the other three. Most of the apparent reduction would have happened anyway. That answer is less flattering than the one in the board pack, and it is the one that survives being questioned. ## Four things that were not available on Monday ### The unit becomes the network You open a case on a structure rather than a file, and the first subject is chosen by computation rather than by claim value. ### Your own knowledge becomes reachable Three adjusters had already noticed. Written observations become searchable beside the records they describe, which is the difference between a note and an asset. ### Underwriting and claims answer together Whether the fraud started at the application is one question over one model, not two exports and a weekend. ### Prevention gets a number you can defend Or an honest refusal where the populations do not compare. Both beat a figure nobody can stand behind. ## Did what we did last year prevent anything? Read the public material of every fraud platform in this market. Detection, scoring, network visualisation, prioritisation, case management. Not one of them measures whether an intervention changed the outcome. That is not an oversight. A prevented fraud leaves no event to count, so answering it means constructing what would have happened otherwise, which is a different discipline from detection and a different piece of mathematics. Nexyron carries 42 causal and decision procedures on the same graph the claims live in. Uplift rather than association. Propensity overlap checked before a number is believed. Interventions, policies and scenarios held as first-class objects, so the delisting you did last March is a thing the model knows about rather than a memory. This is the whole second half of the question, and in this category it is unoccupied. ## Where you cannot pool, this is what is left The leading platforms in this field pool data across carriers, because pooling is where their value comes from. For a large number of carriers that model is simply unavailable: medical records, jurisdictional limits, contractual terms. Where you cannot pool, most of this category is closed to you regardless of budget. Nexyron opens as an application on a machine you already own and starts a full analytical engine on it. Your claims, your notes and every answer built from them stay on your hardware. When one machine is no longer enough, the same engine grows into a cluster inside your own infrastructure. The confidentiality of your claims file becomes a property of the machine rather than a clause in someone's contract. ## Bring one ring you found late Your own claims, on your own machine. Look at when the first claim was paid, and at what your people had already written down before anybody connected them. Request access: https://nexyron.tech/request ## Other industries - [Insurance fraud](https://nexyron.tech/industries/insurance-fraud): A score judges one claim at a time. Rings are built so that every claim survives that test. - [Financial crime and AML](https://nexyron.tech/industries/financial-crime-aml): Everyone suspects which rule has never produced a filing. Nobody can prove it, so it stays. - [Forensic accounting](https://nexyron.tech/industries/forensic-accounting): An intercompany account that reconciles perfectly can hide the fact that nothing was ever transferred. - [Procurement integrity](https://nexyron.tech/industries/procurement-integrity): Across eleven tenders, in rotation. No screen looks there, because nobody joins award data to contract data. - [Internal audit](https://nexyron.tech/industries/internal-audit): It passes every test you run, because every test you run is a test about the entry. - [Insider risk](https://nexyron.tech/industries/insider-risk): Your identity platform knows who can reach the data. Your behaviour platform knows what they did. - [Asset reliability](https://nexyron.tech/industries/asset-reliability): Your prediction platform is trained on a field your technicians do not fill in. - [Supply chain risk](https://nexyron.tech/industries/supply-chain-risk): Your risk platform can tell you a supplier is risky. It has never seen one of your orders. - [Private equity diligence](https://nexyron.tech/industries/private-equity-diligence): Retention improved after they invested in it. Neither fact establishes that the spend caused it.