[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-neo4j-retail-banking":3,"mdc-geb3bt-key":32,"related-org-neo4j-retail-banking":121,"related-repo-neo4j-retail-banking":192},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":23,"topics":27,"repo":28,"sourceUrl":30,"mdContent":31},"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},"neo4j","Neo4j","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fneo4j.png",[12,16,19,22],{"name":13,"slug":14,"type":15},"Security","security","tag",{"name":17,"slug":18,"type":15},"Graph Analysis","graph-analysis",{"name":20,"slug":21,"type":15},"Finance","finance",{"name":9,"slug":8,"type":15},0,"https:\u002F\u002Fgithub.com\u002Fneo4j\u002Fstudio-agent-use-cases","2026-08-18T03:49:47.474584",null,[],{"repoUrl":24,"stars":23,"forks":23,"topics":29,"description":26},[],"https:\u002F\u002Fgithub.com\u002Fneo4j\u002Fstudio-agent-use-cases\u002Ftree\u002FHEAD\u002Fretail-banking","---\nname: retail-banking\ndescription: Detect retail banking transaction fraud rings with graph models, Neo4j schema design, and Cypher query patterns for ring, chronology, and amount-decay checks.\nmetadata:\n  neo4j-card-title: Retail Banking\n  neo4j-card-category: Financial Services\n  neo4j-card-description: Spot suspicious entities and hidden paths in real time, and surface coordinated behavior across accounts and devices.\n  neo4j-icon-category: risk-detection\n---\n\n# Transaction Fraud Ring\n\nUse this skill for retail-banking transaction fraud ring analysis, especially for suspicious circular money movement and APP-fraud style flows.\n\nSource reference:\nhttps:\u002F\u002Fneo4j.com\u002Fdeveloper\u002Findustry-use-cases\u002Ffinserv\u002Fretail-banking\u002Ftransaction-ring\u002F\n\nDo not treat this as legal\u002Fregulatory advice; use it for graph modeling and query-pattern guidance.\n\n## Operational Constraints\n\nFor runnable examples:\n\n- Clarify intended target database\u002Fconnection before execution.\n\n## Response Shape\n\nWhen returning guidance, keep output structured:\n\n```text\nModel assumptions\nCypher (only if requested)\nWhat this detects\nTuning options\nValidation approach\n```\n",{"data":33,"body":39},{"name":4,"description":6,"metadata":34},{"neo4j-card-title":35,"neo4j-card-category":36,"neo4j-card-description":37,"neo4j-icon-category":38},"Retail Banking","Financial Services","Spot suspicious entities and hidden paths in real time, and surface coordinated behavior across accounts and devices.","risk-detection",{"type":40,"children":41},"root",[42,51,57,70,75,82,87,97,103,108],{"type":43,"tag":44,"props":45,"children":47},"element","h1",{"id":46},"transaction-fraud-ring",[48],{"type":49,"value":50},"text","Transaction Fraud Ring",{"type":43,"tag":52,"props":53,"children":54},"p",{},[55],{"type":49,"value":56},"Use this skill for retail-banking transaction fraud ring analysis, especially for suspicious circular money movement and APP-fraud style flows.",{"type":43,"tag":52,"props":58,"children":59},{},[60,62],{"type":49,"value":61},"Source reference:\n",{"type":43,"tag":63,"props":64,"children":68},"a",{"href":65,"rel":66},"https:\u002F\u002Fneo4j.com\u002Fdeveloper\u002Findustry-use-cases\u002Ffinserv\u002Fretail-banking\u002Ftransaction-ring\u002F",[67],"nofollow",[69],{"type":49,"value":65},{"type":43,"tag":52,"props":71,"children":72},{},[73],{"type":49,"value":74},"Do not treat this as legal\u002Fregulatory advice; use it for graph modeling and query-pattern guidance.",{"type":43,"tag":76,"props":77,"children":79},"h2",{"id":78},"operational-constraints",[80],{"type":49,"value":81},"Operational Constraints",{"type":43,"tag":52,"props":83,"children":84},{},[85],{"type":49,"value":86},"For runnable examples:",{"type":43,"tag":88,"props":89,"children":90},"ul",{},[91],{"type":43,"tag":92,"props":93,"children":94},"li",{},[95],{"type":49,"value":96},"Clarify intended target database\u002Fconnection before execution.",{"type":43,"tag":76,"props":98,"children":100},{"id":99},"response-shape",[101],{"type":49,"value":102},"Response Shape",{"type":43,"tag":52,"props":104,"children":105},{},[106],{"type":49,"value":107},"When returning guidance, keep output structured:",{"type":43,"tag":109,"props":110,"children":115},"pre",{"className":111,"code":113,"language":49,"meta":114},[112],"language-text","Model assumptions\nCypher (only if requested)\nWhat this detects\nTuning options\nValidation approach\n","",[116],{"type":43,"tag":117,"props":118,"children":119},"code",{"__ignoreMap":114},[120],{"type":49,"value":113},{"items":122,"total":191},[123,138,153,171,184],{"slug":124,"name":124,"fn":125,"description":126,"org":127,"tags":128,"stars":23,"repoUrl":24,"updatedAt":137},"ev-route-planning","plan electric vehicle routes with graph analysis","Plan electric vehicle routes across a logistics network with Neo4j — battery state-of-charge, charging stops, time-of-day travel, and Cypher 25 stateful path patterns across cities, charging stations, roads, and fleet vehicles.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[129,130,133,134],{"name":17,"slug":18,"type":15},{"name":131,"slug":132,"type":15},"Logistics","logistics",{"name":9,"slug":8,"type":15},{"name":135,"slug":136,"type":15},"Optimization","optimization","2026-08-18T03:49:45.937937",{"slug":139,"name":139,"fn":140,"description":141,"org":142,"tags":143,"stars":23,"repoUrl":24,"updatedAt":152},"identity-validation","resolve identities across systems using graph analysis","Resolve the same person, account, or device across systems and channels using shared and near-duplicate identifiers. 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},[144,147,148,151],{"name":145,"slug":146,"type":15},"Data Engineering","data-engineering",{"name":17,"slug":18,"type":15},{"name":149,"slug":150,"type":15},"Identity","identity",{"name":9,"slug":8,"type":15},"2026-08-18T03:49:46.446479",{"slug":154,"name":154,"fn":155,"description":156,"org":157,"tags":158,"stars":23,"repoUrl":24,"updatedAt":170},"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},[159,162,165,166,169],{"name":160,"slug":161,"type":15},"Data Analysis","data-analysis",{"name":163,"slug":164,"type":15},"Fraud","fraud",{"name":17,"slug":18,"type":15},{"name":167,"slug":168,"type":15},"Insurance","insurance",{"name":9,"slug":8,"type":15},"2026-08-18T03:49:43.272995",{"slug":172,"name":172,"fn":173,"description":174,"org":175,"tags":176,"stars":23,"repoUrl":24,"updatedAt":183},"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},[177,178,179,182],{"name":160,"slug":161,"type":15},{"name":17,"slug":18,"type":15},{"name":180,"slug":181,"type":15},"Healthcare","healthcare",{"name":9,"slug":8,"type":15},"2026-08-18T03:49:43.794045",{"slug":4,"name":4,"fn":5,"description":6,"org":185,"tags":186,"stars":23,"repoUrl":24,"updatedAt":25},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[187,188,189,190],{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},{"name":13,"slug":14,"type":15},5,{"items":193,"total":191},[194,201,208,216,223],{"slug":124,"name":124,"fn":125,"description":126,"org":195,"tags":196,"stars":23,"repoUrl":24,"updatedAt":137},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[197,198,199,200],{"name":17,"slug":18,"type":15},{"name":131,"slug":132,"type":15},{"name":9,"slug":8,"type":15},{"name":135,"slug":136,"type":15},{"slug":139,"name":139,"fn":140,"description":141,"org":202,"tags":203,"stars":23,"repoUrl":24,"updatedAt":152},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[204,205,206,207],{"name":145,"slug":146,"type":15},{"name":17,"slug":18,"type":15},{"name":149,"slug":150,"type":15},{"name":9,"slug":8,"type":15},{"slug":154,"name":154,"fn":155,"description":156,"org":209,"tags":210,"stars":23,"repoUrl":24,"updatedAt":170},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[211,212,213,214,215],{"name":160,"slug":161,"type":15},{"name":163,"slug":164,"type":15},{"name":17,"slug":18,"type":15},{"name":167,"slug":168,"type":15},{"name":9,"slug":8,"type":15},{"slug":172,"name":172,"fn":173,"description":174,"org":217,"tags":218,"stars":23,"repoUrl":24,"updatedAt":183},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[219,220,221,222],{"name":160,"slug":161,"type":15},{"name":17,"slug":18,"type":15},{"name":180,"slug":181,"type":15},{"name":9,"slug":8,"type":15},{"slug":4,"name":4,"fn":5,"description":6,"org":224,"tags":225,"stars":23,"repoUrl":24,"updatedAt":25},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[226,227,228,229],{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},{"name":13,"slug":14,"type":15}]