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GraphDay Stockholm - Levaraging Graph-Technology to fight Financial Fraud

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GraphDay Stockholm February 2017
Stefan Kolmar, Neo Technology

Published in: Technology
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GraphDay Stockholm - Levaraging Graph-Technology to fight Financial Fraud

  1. 1. LEVERAGING GRAPH-TECHNOLOGY TO FIGHT FINANCIAL FRAUD Feb 2017 Stefan Kolmar Director Field Engineering
  2. 2. Retail Banking First-Party Fraud! Opening many lines of credit with no intention 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: http://www.experian.com/assets/decision-analytics/white-papers/first-partyfraud-wp.pdf! (2)  Experian: http://www.experian.com/assets/decision-analytics/white-papers/first-partyfraud-wp.pdf! (3) Business Insider: http://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! 555-555- 5555! 123 NW 1st Street! San Francisco, CA!555-555- 5555! Skimming Person A! Person B! Location A! Location B! Phone Number Duplicate Use 555-555-5 555! Person A! Person B! Suspect eCommerce Person A! Person B! Location C! IP address!
  4. 4. Fraud Demo – Part I (generic)! •  Fraud scenario covering Retail Fraud use cases! •  Data set contains operational data! •  Constant data load –> injecting fraud cases -> generate alerts! •  Capability to export data of detected fraud for further investigation! Neo4j! App Server! Fraud Detection! Web App! Fraud App! Browser! UX: TestDataG en! Alert generated!
  5. 5. Demo!
  6. 6. Why using GraphDB / Neo4j for Fraud Detection?! •  Graphs are intuitive to understand! •  Schema free - > Flexibility! •  Nodes can vary depending on time / usage / semantic! •  Adopt dynamic changes! •  Agile Development! •  High productivity and rapid implementation ! •  No “RDBMS-waterfall-high-investment-trap” ! •  Taking advantage of the full value of connected data and data relationships! •  Traversing the graph compared to self joins in RDBMS! •  Near real time response times! •  Preventing fraud rather than detecting after the fact!
  7. 7. •  Usage scenario Fraud Analyst: ! •  Potential fraud case detected! •  Enriched with data from various sources containing data on fraud suspect! •  Trigger human and/or automated reactions! Fraud Demo – Part II Neo4j! Web App! RDBMS! (Oracle, MySQL, DB2, HANA …)! Management Console! (E.g BI Tools such as ! Tableau, Qlik, BO, MicroStrategy etc)! Fraud Analyst Machine2Machine ! generated actions! Alert! Incoming Events! CRM System! ! ! ! ! ! Operational System! ! ! Data! Integration! External Data!
  8. 8. Using Neo as the foundation of a fraud solution in your architecture! Step 1: Set up Data Integration! Step 2: Visualize Data in BI Tool!
  9. 9. Conclusions! •  Fraud as one use case to provide full value of connected data within the entire organization! •  Neo4j as the foundation to do 360 degree fraud detection and prevention! •  Neo4j to extend your existing environment while protecting your investments! •  Neo4j provides best value integrated in the entire environment! •  Neo4j as the foundation for generating real time alerts to trigger automated or manual interventions! !
  10. 10. A deeper look into the database!
  11. 11. A brief look into the data model ….!
  12. 12. Fraud Demo! Solutions powered with Neo4j ! 2017! ! Stefan.Kolmar@neotechnology.com!
  13. 13. THANK YOU!

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