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Neo4j GraphTalks - Fighting fraud with Neo4j - Kees Vegter, Neo4j

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The Neo4j graph database is the fastest growing database engine in the market and has hundreds of customer references across Europe and globally, solving significant technology problems for large Enterprises in Finance, Telco, Retail, Utilities, Logistics and Internet sectors. Typical use cases are Recommendations, Fraud Detection, MDM, Network and Software Analysis and Optimization, Identity and Access Management.

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Neo4j GraphTalks - Fighting fraud with Neo4j - Kees Vegter, Neo4j

  1. 1. Fighting Fraud with Neo4j Neo4j Graphtalk Oslo, June 7, 2017 ! Kees Vegter, Field Engineer kees@neotechnology.com!
  2. 2. Agenda •  Who are Today’s Fraudsters! •  Fraud Detection! •  Neo4j Demo! •  How Neo4j Fits in a Typical Architecture! •  Summary! •  Q&A!
  3. 3. Who Are Today’s Fraudsters?
  4. 4. Organized in groups Synthetic Identities Stolen Identities Hijacked Devices Who Are Today’s Fraudsters?
  5. 5. Types of Fraud •  Credit Card Fraud •  Rogue Merchants •  Fraud Rings •  Insurance Fraud •  eCommerce Fraud •  Fraud we don’t know about yet…
  6. 6. Digitized and Analog! World of Fraud Constantly Evolving! Few and Many Players! “One Step Ahead”! Simple and Complex!
  7. 7. Fraud Detection data-perspective!
  8. 8. Raw Data
  9. 9. Anomalies
  10. 10. Patterns
  11. 11. Patterns
  12. 12. 1) Detect 2) Respond Fraud Prevention is About Reacting to Patterns (And doing it fast!) !
  13. 13. Relational Database Choosing Underlying Technology
  14. 14. Data Modelled as a Graph! Graph Database
  15. 15. Fraud Detection methods!
  16. 16. “Don’t consider traditional technology adequate to keep up with criminal trends” Market Guide for Online Fraud Detection, April 27, 2015
  17. 17. 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
  18. 18. ! 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
  19. 19. INVESTIGATE Revolving Debt! Number of Accounts! INVESTIGATE Normal behavior Fraud Detection with Discrete Analysis
  20. 20. Revolving Debt! Number of Accounts! Normal behavior Fraudulent pattern Fraud Detection with Connected Analysis
  21. 21. 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. Cross Channel Analysis of anomaly behavior correlated across channels! 4. Entity Linking Analysis of relationships to detect organized crime and collusion! 5. Augmented Fraud Detection
  22. 22. Examples of Prevalent Fraud Types
  23. 23. Fraud Rings
  24. 24. ACCOUNT HOLDER 2 Modeling a fraud ring as a graph ACCOUNT HOLDER 1 ACCOUNT HOLDER 3
  25. 25. ACCOUNT HOLDER 2 ACCOUNT HOLDER 1 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT PHONE NUMBER UNSECURED LOAN SSN 2 UNSECURED LOAN Modeling a fraud ring as a graph
  26. 26. ACCOUNT HOLDER 2 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT ADDRESS PHONE NUMBER PHONE NUMBER SSN 2 UNSECURED LOAN SSN 2 UNSECURED LOAN Modeling a fraud ring as a graph ACCOUNT HOLDER 1
  27. 27. Credit Card Fraud
  28. 28. Example #1 “Credit Card Testing”
  29. 29. USE! ISSUES! Terminal ATM- skimming Data Breach Card Holder Card Issuer Fraudster USE! MAKES! $4000 AT! $5MAKES! $1 0 MAKES! $2 MAKES! Testing Merchants AT!MAKES! Tx
  30. 30. Example #2 “Fraud Origination and Assessing Loss Magnitude”
  31. 31. TxTx Tx TxTx Tx Tx TxTxTx TxJohn!
  32. 32. Tx $2000 TxTx Tx Tx TxTxTxTx Tx Tx Computer! Store! John!
  33. 33. Tx $2000 Tx Tx $25$10$4 TxTx Tx Tx TxTxTx Computer! Store! John! Gas Station!
  34. 34. Tx Tx $2000 Tx Tx $25$10$4 TxTx Tx Tx TxTxTx Computer! Store! John! Gas Station! Sheila! Tx $2 TxTxSheila! TxTxTx Tx Tx TxTx $3000 Tx Jewelry! Store!Tx $3
  35. 35. Tx Tx $2000 Tx Tx $25$10$4 TxTx Tx Tx TxTxTx Computer! Store! John! Gas Station! Sheila! Tx $2 TxTxSheila! TxTxTx Tx Tx TxTx $3000 Tx Jewelry! Store!Tx $3 Robert! TxTxTx Tx TxTx TxTxTx Tx Tx
  36. 36. TxTx $2 TxTx Tx $2000 Tx Tx $25$10$4 TxTx Tx Tx TxTxTx Computer! Store! John! Gas Station! Sheila! Robert! $3 Karen! TxTxTx Tx Tx TxTx $3000 Tx Jewelry! Store!Tx $3 TxTxTx Tx Tx TxTx TxTx TxTx TxTx Tx Tx TxTx $8 $12 Tx $1500 Furniture! Store! Tx Tx Tx
  37. 37. Fraud Demo
  38. 38. Show case the (near) real time fraud detection.! •  React on fraud patterns coming in the db! •  Solution architecture! ! •  An example application showing the power of graph visualization in your business application.! •  There are a lot of visualization products available today...! Neo4j! Transactions! Demo! Application!
  39. 39. Demo
  40. 40. About Realtime Processing! •  For real time checking we are just as fast as in the demo even with big databases. ! •  In real time we know the transaction context. and we can jump to the right place into the graph to find/check for fraud patterns! !
  41. 41. Concluding! •  Effectively find Fraud Patterns because we have the Cypher Query Language! •  We actually store the connections in the database.! •  Index Free Adjacency! •  Having the possibility to add graph-visualization to your business applications gives you better insight in your connected data.! MATCH (boss)-[:MANAGES*0..3]->(sub), (sub)-[:MANAGES*1..3]->(report) WHERE boss.name = “John Doe” RETURN sub.name AS Subordinate, count(report) AS Total
  42. 42. Anti Money Laundering! •  Seeking for deep patterns (who sends money to who)! •  Using also shared attributes like in Fraud Rings: ! •  Names! •  Email! •  Phone! •  Address! •  SSN! •  IDNO....! •  Transaction Context!
  43. 43. How Neo4j fits in
  44. 44. Money Transferring! Purchases! Bank Services! Relational database Develop Patterns! Data Science-team + Good for Discrete Analysis – No Holistic View of Data-Relationships – Slow query speed for connections
  45. 45. Money Transferring! Purchases! Bank Services! Relational database Data Lake +  Good for Map Reduce +  Good for Analytical Workloads – No holistic view – Non-operational workloads – Weeks-to-months processes Develop Patterns! Data Science-team Merchant Data! Credit Score Data! Other 3rd Party Data!
  46. 46. Money Transferring! Purchases! Bank Services! Neo4j powers 360° view of transactions in real-time Neo4j Cluster SENSE Transaction stream RESPOND Alerts & notification LOAD RELEVANT DATA Relational database Data Lake Visualization UI Fine Tune Patterns Develop Patterns! Data Science-team Merchant Data! Credit Score Data! Other 3rd Party Data!
  47. 47. Money Transferring! Purchases! Bank Services! Neo4j powers 360° view of transactions in real-time Neo4j Cluster SENSE Transaction stream RESPOND Alerts & notification LOAD RELEVANT DATA Relational database Data Lake Visualization UI Fine Tune Patterns Develop Patterns! Data Science-team Merchant Data! Credit Score Data! Other 3rd Party Data! Data-set used to explore new insights
  48. 48. Concluding! •  The Neo4j graph database is a natural fit for finding fraud patterns in the data in real time (and non-real time)! Delayed Data Analysis! Real Time Connected Analysis!
  49. 49. Valuable Resources! neo4jsandbox.com! https://neo4j.com/use-cases/fraud-detection/! neo4j.com/product! Sandbox Fraud Detection Product
  50. 50. Neo4j!Transaction! Tomcat! Web App! Browser App! Software Architecture Demo!

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