GraphDay Stockholm - Levaraging Graph-Technology to fight Financial Fraud
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GraphDay Stockholm February 2017 Stefan Kolmar, Neo Technology
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GraphDay Stockholm - Levaraging Graph-Technology to fight Financial Fraud
- 1. LEVERAGING GRAPH-TECHNOLOGY TO FIGHT FINANCIAL FRAUD Feb 2017 Stefan Kolmar Director Field Engineering
- 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. 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. 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. Demo!
- 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. • 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. 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. 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. A deeper look into the database!
- 11. A brief look into the data model ….!
- 12. Fraud Demo! Solutions powered with Neo4j ! 2017! ! Stefan.Kolmar@neotechnology.com!
- 13. THANK YOU!