[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-neo4j-insurance-claims-fraud":3,"mdc-ilydnu-key":35,"related-repo-neo4j-insurance-claims-fraud":311,"related-org-neo4j-insurance-claims-fraud":380},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":26,"topics":30,"repo":31,"sourceUrl":33,"mdContent":34},"insurance-claims-fraud","detect insurance claims fraud using graph analysis","Detect insurance claims fraud by modelling claimants, medical professionals, vehicles, and claims as a graph. Provides a graph model, bundled sample data, and Cypher for repeat claimants, unusual medical-professional activity, and vehicles reused across claims.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"neo4j","Neo4j","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fneo4j.png",[12,16,19,20,23],{"name":13,"slug":14,"type":15},"Graph Analysis","graph-analysis","tag",{"name":17,"slug":18,"type":15},"Fraud","fraud",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"Data Analysis","data-analysis",{"name":24,"slug":25,"type":15},"Insurance","insurance",0,"https:\u002F\u002Fgithub.com\u002Fneo4j\u002Fstudio-agent-use-cases","2026-08-18T03:49:43.272995",null,[],{"repoUrl":27,"stars":26,"forks":26,"topics":32,"description":29},[],"https:\u002F\u002Fgithub.com\u002Fneo4j\u002Fstudio-agent-use-cases\u002Ftree\u002FHEAD\u002Finsurance-claims-fraud","---\nname: insurance-claims-fraud\ndescription: Detect insurance claims fraud by modelling claimants, medical professionals, vehicles, and claims as a graph. Provides a graph model, bundled sample data, and Cypher for repeat claimants, unusual medical-professional activity, and vehicles reused across claims.\nmetadata:\n  neo4j-card-title: Insurance Claims Fraud\n  neo4j-card-category: Insurance & Risk\n  neo4j-card-description: Uncover fraud rings and suspicious claims through the connections between claimants, doctors, vehicles, and claims.\n  neo4j-icon-category: risk-detection\n---\n\n# Insurance Claims Fraud\n\nUse this skill for insurance fraud analysts and developers investigating claims fraud\n(staged accidents, exaggerated injuries, inflated costs, crash-for-cash rings) by exploring\nthe relationships between claimants, medical professionals, vehicles, and claims.\n\nSource reference:\n\n- \u003Chttps:\u002F\u002Fneo4j.com\u002Fdeveloper\u002Findustry-use-cases\u002Finsurance\u002Fclaims-fraud\u002F>\n\nDisclaimer: this is an illustrative, synthetic model for demonstration. Real claims fraud\ndetection needs validated data, thresholds tuned to the portfolio, and human review before\nany decision. Do not present model choices, thresholds, or query behaviour as proven guidance.\n\n## Introducing this package\n\nWhen the user first opens this package, greet them with a short introduction in your own\nwords — don't recite this file. Convey:\n\n- The core idea: fraudulent claims (staged accidents, exaggerated injuries, inflated repair\n  costs) tend to hide in the relationships between parties rather than in any single record —\n  the same vehicle across several claims, one doctor tied to an unusual share of high-value\n  claims, a claimant filing repeatedly.\n- The bundled sample data is synthetic and deliberately seeds each of those patterns, so the\n  queries in `QUERIES.md` return results straight away.\n- A schema diagram is available to show when explaining the model:\n  \u003Chttps:\u002F\u002Fneo4j.com\u002Fdeveloper\u002Findustry-use-cases\u002F_images\u002Finsurance\u002Finsurance-claims-fraud-schema.svg>\n- What you can help with: explaining the model, walking through the query patterns (repeat\n  claimants, unusual medical-professional activity, reused vehicles), importing the sample\n  data or their own, and running Cypher once a database is connected.\n\nEnd with a clear next step, such as asking whether they'd like to explore the model or start\nimporting data.\n\n## Model\n\n- Nodes: `Claimant {name}`, `MedicalProfessional {name}`, `Claim {claimID, date, amountClaimed}`, `Vehicle {VIN}`.\n- Relationships: `(Claimant)-[:HAS_CLAIM]->(Claim)`, `(Claim)-[:TREATED_BY]->(MedicalProfessional)`, `(Claimant)-[:OWNS]->(Vehicle)`, `(Vehicle)-[:INVOLVED_IN]->(Claim)`, `(MedicalProfessional)-[:TREATS]->(Claimant)`.\n- Full schema, mappings, and sample CSVs are in `GRAPH_MODEL.json` and `sample-data\u002F`; runnable Cypher with expected results is in `QUERIES.md`.\n\n## Operational Constraints\n\n- Prefer the bundled `sample-data\u002F` Import flow. No post-import setup is required — do not improvise indexes or constraints after Import.\n- The Graph Data Science queries in `QUERIES.md` require the GDS library and are unproven; flag that before running.\n- Thresholds in the queries (claim count, total amount) are illustrative parameters; tune per portfolio.\n\n## Response Shape\n\nWhen returning guidance, keep output structured:\n\n```text\nModel assumptions\nCypher (if requested)\nWhat this detects\nTuning options\nValidation approach\n```\n",{"data":36,"body":42},{"name":4,"description":6,"metadata":37},{"neo4j-card-title":38,"neo4j-card-category":39,"neo4j-card-description":40,"neo4j-icon-category":41},"Insurance Claims Fraud","Insurance & Risk","Uncover fraud rings and suspicious claims through the connections between claimants, doctors, vehicles, and claims.","risk-detection",{"type":43,"children":44},"root",[45,52,58,63,79,84,91,96,134,139,145,250,256,288,294,299],{"type":46,"tag":47,"props":48,"children":49},"element","h1",{"id":4},[50],{"type":51,"value":38},"text",{"type":46,"tag":53,"props":54,"children":55},"p",{},[56],{"type":51,"value":57},"Use this skill for insurance fraud analysts and developers investigating claims fraud\n(staged accidents, exaggerated injuries, inflated costs, crash-for-cash rings) by exploring\nthe relationships between claimants, medical professionals, vehicles, and claims.",{"type":46,"tag":53,"props":59,"children":60},{},[61],{"type":51,"value":62},"Source reference:",{"type":46,"tag":64,"props":65,"children":66},"ul",{},[67],{"type":46,"tag":68,"props":69,"children":70},"li",{},[71],{"type":46,"tag":72,"props":73,"children":77},"a",{"href":74,"rel":75},"https:\u002F\u002Fneo4j.com\u002Fdeveloper\u002Findustry-use-cases\u002Finsurance\u002Fclaims-fraud\u002F",[76],"nofollow",[78],{"type":51,"value":74},{"type":46,"tag":53,"props":80,"children":81},{},[82],{"type":51,"value":83},"Disclaimer: this is an illustrative, synthetic model for demonstration. Real claims fraud\ndetection needs validated data, thresholds tuned to the portfolio, and human review before\nany decision. Do not present model choices, thresholds, or query behaviour as proven guidance.",{"type":46,"tag":85,"props":86,"children":88},"h2",{"id":87},"introducing-this-package",[89],{"type":51,"value":90},"Introducing this package",{"type":46,"tag":53,"props":92,"children":93},{},[94],{"type":51,"value":95},"When the user first opens this package, greet them with a short introduction in your own\nwords — don't recite this file. 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Covers cross-channel identity resolution, golden-record\u002FMDM matching, synthetic identity fraud, household linking, and address geocoding with spatial functions.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[334,337,338,341],{"name":335,"slug":336,"type":15},"Data Engineering","data-engineering",{"name":13,"slug":14,"type":15},{"name":339,"slug":340,"type":15},"Identity","identity",{"name":9,"slug":8,"type":15},"2026-08-18T03:49:46.446479",{"slug":4,"name":4,"fn":5,"description":6,"org":344,"tags":345,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[346,347,348,349,350],{"name":21,"slug":22,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"name":24,"slug":25,"type":15},{"name":9,"slug":8,"type":15},{"slug":352,"name":352,"fn":353,"description":354,"org":355,"tags":356,"stars":26,"repoUrl":27,"updatedAt":363},"patient-journey","analyze patient journeys using graph models","Model and analyze patient journeys through a healthcare system with Neo4j — longitudinal care pathways, comorbidity, treatment-pattern queries across patients, encounters, diagnoses, and more.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[357,358,359,362],{"name":21,"slug":22,"type":15},{"name":13,"slug":14,"type":15},{"name":360,"slug":361,"type":15},"Healthcare","healthcare",{"name":9,"slug":8,"type":15},"2026-08-18T03:49:43.794045",{"slug":365,"name":365,"fn":366,"description":367,"org":368,"tags":369,"stars":26,"repoUrl":27,"updatedAt":378},"retail-banking","detect retail banking fraud rings","Detect retail banking transaction fraud rings with graph models, Neo4j schema design, and Cypher query patterns for ring, chronology, and amount-decay checks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[370,373,374,375],{"name":371,"slug":372,"type":15},"Finance","finance",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},{"name":376,"slug":377,"type":15},"Security","security","2026-08-18T03:49:47.474584",5,{"items":381,"total":379},[382,389,396,404,411],{"slug":314,"name":314,"fn":315,"description":316,"org":383,"tags":384,"stars":26,"repoUrl":27,"updatedAt":327},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[385,386,387,388],{"name":13,"slug":14,"type":15},{"name":321,"slug":322,"type":15},{"name":9,"slug":8,"type":15},{"name":325,"slug":326,"type":15},{"slug":329,"name":329,"fn":330,"description":331,"org":390,"tags":391,"stars":26,"repoUrl":27,"updatedAt":342},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[392,393,394,395],{"name":335,"slug":336,"type":15},{"name":13,"slug":14,"type":15},{"name":339,"slug":340,"type":15},{"name":9,"slug":8,"type":15},{"slug":4,"name":4,"fn":5,"description":6,"org":397,"tags":398,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[399,400,401,402,403],{"name":21,"slug":22,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"name":24,"slug":25,"type":15},{"name":9,"slug":8,"type":15},{"slug":352,"name":352,"fn":353,"description":354,"org":405,"tags":406,"stars":26,"repoUrl":27,"updatedAt":363},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[407,408,409,410],{"name":21,"slug":22,"type":15},{"name":13,"slug":14,"type":15},{"name":360,"slug":361,"type":15},{"name":9,"slug":8,"type":15},{"slug":365,"name":365,"fn":366,"description":367,"org":412,"tags":413,"stars":26,"repoUrl":27,"updatedAt":378},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[414,415,416,417],{"name":371,"slug":372,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},{"name":376,"slug":377,"type":15}]