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GraphTalks Frankfurt - Leveraging Graph-Technology to fight financial fraud

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GraphTalks Frankfurt
Stefan Kolmar, Neo4j

Publicado en: Tecnología
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GraphTalks Frankfurt - Leveraging Graph-Technology to fight financial fraud

  1. 1. LEVERAGING GRAPH-TECHNOLOGY TO FIGHT FINANCIAL FRAUD November 2017 Stefan Kolmar VP Field Engineering
  2. 2. AGENDA •  Meet today’s fraudsters •  Traditional fraud detection methods •  Using connected analysis for real-time fraud detection •  Demo •  Summary
  3. 3. The Impact of Fraud The payment card fraud alone, constitutes for over 16 billion dollar in losses for the bank-sector in the US. $16Bpayment card fraud in 2014* Banking $32Byearly e-commerce fraud** Fraud in E-commerce is estimated to cost over 32 billion dollars annually is the US.. E-commerce The impact of fraud on the insurance industry is estimated to be $80 billion annually in the US. Insurance $80Bestimated yearly impact*** *) Business Wire: http://www.businesswire.com/news/home/20150804007054/en/Global-Card-Fraud-Losses-Reach-16.31-Billion#.VcJZlvlVhBc **) E-commerce expert Andreas Thim, Klarna, 2015 ***) Coalition against insurance fraud: http://www.insurancefraud.org/article.htm?RecID=3274#.UnWuZ5E7ROA
  4. 4. Who Are Today’s Fraudsters?
  5. 5. Organized in groups Synthetic Identities Stolen Identities Who Are Today’s Fraudsters? Hijacked Devices
  6. 6. “Don’t consider traditional technology adequate to keep up with criminal trends” Market Guide for Online Fraud Detection, April 27, 2015!
  7. 7. Endpoint-Centric Analysis of users and their end-points! 1. Navigation Centric Analysis of navigation behavior and suspect patterns! 2. Account-Centric Analysis of anomaly behavior by channel! 3. PC:s Mobile Phones IP-addresses User ID:s Comparing Transaction Identity Vetting Traditional Fraud Detection Methods
  8. 8. ! Unable to detect! •  Fraud rings! •  Fake IP-adresses! •  Hijacked devices! •  Synthetic Identities! •  Stolen Identities! •  And more…! Weaknesses DISCRETE ANALYSIS Endpoint-Centric Analysis of users and their end-points! 1. Navigation Centric Analysis of navigation behavior and suspect patterns! 2. Account-Centric Analysis of anomaly behavior by channel! 3. Traditional Fraud Detection Methods
  9. 9. INVESTIGATE Revolving Debt! Number of Accounts! INVESTIGATE Normal behavior Fraud Detection With Discrete Analysis
  10. 10. Revolving Debt! Number of Accounts! Normal behavior Fraud Detection With Connected Analysis Fraudulent pattern
  11. 11. CONNECTED ANALYSIS Augmented Fraud Detection Endpoint-Centric Analysis of users and their end-points! Navigation Centric Analysis of navigation behavior and suspect patterns! Account-Centric Analysis of anomaly behavior by channel! DISCRETE ANALYSIS 1. 2. 3. Cross Channel Analysis of anomaly behavior correlated across channels! 4. Entity Linking Analysis of relationships to detect organized crime and collusion! 5.
  12. 12. ACCOUNT HOLDER 2 Modeling a fraud ring as a graph ACCOUNT HOLDER 1 ACCOUNT HOLDER 3
  13. 13. ACCOUNT HOLDER 2 Modeling a fraud ring as a graph ACCOUNT HOLDER 1 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT PHONE NUMBER UNSECURED LOAN SSN 2 UNSECURED LOAN
  14. 14. ACCOUNT HOLDER 2 Modeling a fraud ring as a graph ACCOUNT HOLDER 1 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT ADDRESS PHONE NUMBER PHONE NUMBER SSN 2 UNSECURED LOAN SSN 2 UNSECURED LOAN
  15. 15. ACCOUNT HOLDER 2 Modeling a fraud ring as a graph ACCOUNT HOLDER 1 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT ADDRESS PHONE NUMBER PHONE NUMBER SSN 2 UNSECURED LOAN SSN 2 UNSECURED LOAN SYNTETIC PERSON 2 SYNTHETIC PERSON 1
  16. 16. FRAUD DEMO
  17. 17. USING NEO4j FOR REAL-TIME CONNECTED ANALYSIS
  18. 18. Account-Centric Analysis of anomaly behavior correlated across channels! 4. Entity Linking Analysis of relationships to detect organized crime and collusion! 5. CONNECTED ANALYSIS Endpoint-Centric Analysis of users and their end-points! Navigation Centric Analysis of navigation behavior and suspect patterns! Account-Centric Analysis of anomaly behavior by channel! DISCRETE ANALYSIS 1. 2. 3. Augment Fraud Detection with Neo4j Traditional Vendors
  19. 19. ACCEPT / DECLINE MANUAL User/Transaction! CONNECTED ANALYSIS User/Transaction! ACCEPT / DECLINE(DISCRETE ANALYSIS) + User/Transaction! (sub-second performance to any data size and connection)! ACCEPT / DECLINE REAL TIME TRADITIONAL VENDORS (DISCRETE ANALYSIS) (DISCRETE ANALYSIS) ACCEPT / DECLINE How Neo4j fits in
  20. 20.   Detect & prevent fraud in real-time!   Faster credit risk analysis and transactions!   Reduce chargebacks!   Quickly adapt to new methods of fraud! Why Neo4j?! Who’s using it?! Financial institutions use Neo4j to: FINANCE Government Online Retail
  21. 21. •  Today’s fraudsters are organized and highly sophisticated •  Legacy technology does not detect fraud sufficiently and in real-time •  Graph-databases enable you to discover fraudulent patterns in real- time •  Augment your current fraud detection infrastructure with connected analysis KEY TAKE AWAYS
  22. 22. Using Neo to detect Fraud
  23. 23. 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!
  24. 24. 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!
  25. 25. 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!
  26. 26. Demo!
  27. 27. 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!
  28. 28. •  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!
  29. 29. 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!
  30. 30. 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! !
  31. 31. A deeper look into the database!
  32. 32. A brief look into the data model ….!
  33. 33. Fraud Demo! Solutions powered with Neo4j ! 2017! ! Stefan.Kolmar@neo4j.com!
  34. 34. THANK YOU!

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