How to Build a Fraud Detection Solution with Neo4j
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Joe Depeau, Neo4j
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How to Build a Fraud Detection Solution with Neo4j
- 1. How to Build a Fraud Detection Solution with Neo4j Joe Depeau Sr. Presales Consultant, UK 18th July, 2018 @joedepeau http://linkedin.com/in/joedepeau
- 2. • Who are Today’s Fraudsters? • Fraud Detection from a Data Modelling Perspective • How to Fight Fraud Rings with Graphs • A Closer Look at Credit Card Fraud • How Neo4j Fits in a Typical Architecture • Demo • Summary • Q & A 2 Agenda
- 4. 4 Who Are Today’s Fraudsters?
- 5. 5 Organized in groups Synthetic Identities Stolen Identities Hijacked Devices Who Are Today’s Fraudsters?
- 6. 6 Types of Fraud • Credit Card Fraud • Rogue Merchants • Fraud Rings • Insurance Fraud • eCommerce Fraud • Fraud we don’t know about yet…
- 7. 7 Digitized and Analog World of Fraud Constantly Evolving Few and Many Players “One Step Ahead” Simple and Complex
- 8. Fraud Detection (from a data modelling perspective) 8
- 9. 9 Raw Data
- 10. 10 Anomalies
- 11. 11 Patterns
- 12. 12 1) Detect 2) Respond Fraud Prevention is About Reacting to Patterns (And doing it fast!)
- 14. 14 Data Modelled as a Graph! Graph Database
- 15. 15 ACCOUNT HOLDER 2 Modeling a fraud ring as a graph ACCOUNT HOLDER 1 ACCOUNT HOLDER 3
- 16. 16 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
- 17. 17 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
- 18. How to Fight Fraud Rings with Graphs 18
- 19. 19 “Don’t consider traditional technology adequate to keep up with criminal trends” Market Guide for Online Fraud Detection, April 27, 2015
- 20. 20 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 Layered Model for Fraud Prevention (https://www.gartner.com/newsroom/id/1695014)
- 21. 21 Unable to detect • Fraud rings • Fake IP-addresses • 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 Layered Model for Fraud Prevention (https://www.gartner.com/newsroom/id/1695014)
- 22. 22 INVESTIGATE Revolving Debt Number of Accounts INVESTIGATE Normal behavior Fraud Detection with Discrete Analysis
- 23. 23 Revolving Debt Number of Accounts Normal behavior Fraudulent pattern Fraud Detection with Connected Analysis
- 24. 24 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 Layered Model for Fraud Prevention (https://www.gartner.com/newsroom/id/1695014)
- 25. 25
- 26. Blank Slide 26
- 27. A Closer Look at Credit Card Fraud 27
- 28. 28 Manual skimming of an ATM Sophisticated Data Breaches Retrieval of Credit Card Information Rogue Merchant
- 29. 29 USE ISSUES Terminal ATM- skimming Data Breach Card Holder Card Issuer Fraudster USE $5MAKES $10 MAKES $2 MAKES MAKES $4000 AT Testing Merchants ATMAKES Tx
- 30. 30 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 StoreTx $3 TxTxTx Tx Tx TxTx TxTx TxTx TxTx Tx Tx TxTx $8 $12 Tx $1500 Furniture Store Tx Tx Tx
- 31. How Neo4j Fits in a Typical Architecture 31
- 32. 32 Money Transferring Purchases Bank Services Relational databases Develop Patterns Data Science team + Good for Discrete Analysis – No Holistic View of Data-Relationships – Slow query speed for connections
- 33. 33 Money Transferring Purchases Bank Services Relational databases 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
- 34. 34 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 databases Data Lake Visualization UI Fine Tune Patterns Develop Patterns Data Science team Merchant Data Credit Score Data Other 3rd Party Data LOAD RELEVANT DATA
- 35. 35 Money Transferring Purchases Bank Services Neo4j powers 360° view of transactions in real-time Neo4j Cluster SENSE Transaction stream RESPOND Alerts & notification Relational databases 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 LOAD RELEVANT DATA LOAD RELEVANT DATA
- 36. Demo 36
- 37. Q & A 37
- 38. 38 Valuable Resources! neo4jsandbox.com https://neo4j.com/use-cases/fraud-detection/ neo4j.com/product Sandbox Fraud Detection Product