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Fraud Detection with Neo4j

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Fraud Detection with Neo4j

  1. 1. Fraud Demo Solu%ons powered with Neo4j 2015 Stefan Kolmar Neo Technologies
  2. 2. Retail Banking First-Party Fraud Opening many lines of credit with no inten5on of paying them back Causing High Impact •  Tens of billions of dollars lost every year by U.S. Banks.(1) •  25% of total consumer credit charge-offs in the United States.(2) •  10% to 20% of unsecured bad debt at leading U.S. and European banks is misclassified, and is actually first-party fraud.(3) (1) Experian: hQp://www.experian.com/assets/decision-analy5cs/white-papers/first-partyfraud-wp.pdf (2) Experian: hQp://www.experian.com/assets/decision-analy5cs/white-papers/first-partyfraud-wp.pdf (3) Business Insider: hQp://www.businessinsider.com/how-to-use-social-networks-in-the-fight-against-first-party-fraud-2011-3
  3. 3. Detec%ng Fraud Rings SSN1 123 NW 1st Street San Francisco, CA 555-5 55-55 55 123 NW 1st Street San Francisco, CA 555-5 55-55 55 Skimming Person A Person B Loca5on A Loca5on B Phone Number Duplicate Use 555-555-5 555 Person A Person B Suspect eCommerce Person A Person B Loca5on C IP address
  4. 4. Fraud Demo – Part I (generic) •  Fraud scenario covering Retail Fraud use cases •  Data set contains opera5onal data •  Constant data load –> injec5ng fraud cases -> generate alerts •  Capability to export data of detected fraud for further inves5ga5on Neo4j App Server Fraud Detec5on Web App Fraud App Browser UX: TestDataGen Alert generated
  5. 5. Demo
  6. 6. Why using GraphDB / Neo4j for Fraud? •  Agile Development •  High produc5vity and rapid implementa5on •  No “RDBMS-waterfall-high-investment-trap” •  Taking advantage of the full value of connected data •  Traversing the graph compared to self joins in RDBMS •  Near real 5me response 5mes •  Preven5ng fraud rather than detec5ng ader the fact •  Schema free •  Nodes can vary depending on 5me / usage / seman5c
  7. 7. •  Usage scenario Fraud Analyst: •  Poten5al fraud case detected •  Enriched with data from various sources containing data on fraud suspect •  Trigger human and/or automated reac5ons Fraud Demo – Part II Neo4j Web App Data Integra5on RDBMS (Oracle, MySQL, DB2, HANA …) Management Console (E.g BI Tools such as Tableau, Qlik, BO, MicroStrategy etc) Fraud Analyst Machine2Machine generated ac5ons Alert Incoming Events CRM System Opera5onal System External Data
  8. 8. Using Neo as the founda%on of a fraud solu%on in your architecture Step 1: Set up Data Integra5on Step 2: Visualize Data in BI Tool
  9. 9. Conclusions •  Fraud as one use case to provide full value of connected data within the en5re organiza5on •  Neo4j as the founda5on to do 360 degree fraud detec5on and preven5on •  Neo4j to extend your exis5ng environment while protec5ng your investments •  Neo4j provides best value integrated in the en5re environment •  Neo4j as the founda5on for genera5ng real 5me alerts to trigger automated or manual interven5ons
  10. 10. Encore
  11. 11. … One more thing ...
  12. 12. A brief look into the data model ….
  13. 13. Fraud Demo Solu%ons powered with Neo4j 2015 Stefan.Kolmar@neotechnology.com

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