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Webinar: Stop Complex Fraud in its Tracks with Neo4j

Financial Services firms are having difficulty with traditional fraud prevention measures that focus on discrete data points such as specific accounts, individuals, devices or IP addresses. However, today’s sophisticated fraudsters escape detection by using sophisticated techniques like card testing, masquerading as a legitimate merchant, skimming cards at vulnerable merchants, forming fraud rings comprised of stolen and synthetic identities, etc.

To uncover such fraud rings, it is essential to look beyond individual data points to the connections that link them. Big Data Platforms and Data Science teams have been deployed to get rid of this menace but it takes weeks and months to uncover these patterns leading to high risk levels and inability to catch the fraudsters before they move on.

Join this webinar to find out why enterprise organizations use Neo4j to augment their existing fraud detection capabilities to combat a variety of financial crimes – and doing so in real-time.

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Webinar: Stop Complex Fraud in its Tracks with Neo4j

  1. 1. Stop Complex Fraud in its Tracks with Neo4j Neo4j Webinar, March 29, 2017
  2. 2. Ryan Boyd Developer Relations @ Neo4j Nav Mathur Sr. Director Global Solutions @ Neo4j Alessandro Svensson Solutions Marketing @ Neo4j
  3. 3. Agenda • Who are Today’s Fraudsters • How to Fight Fraud Rings with Graphs • Different Types of Credit Card Fraud & Neo4j Demo • How Neo4j Fits in a Typical Architecture • Summary • Q&A
  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. 8. Fraud Detection (From a data-modeling perspective)
  9. 9. Raw Data
  10. 10. Anomalies
  11. 11. Anomalies hidden in “normal behavior”
  12. 12. Patterns
  13. 13. Patterns
  14. 14. 1) Detect 2) Respond Fraud Prevention is About Reacting to Patterns (And doing it fast!)
  15. 15. Relational Database Choosing Underlying Technology
  16. 16. Data Modelled as a Graph! Graph Database
  17. 17. Examples of Prevalent Fraud Types
  18. 18. Fraud Rings
  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
  21. 21. 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
  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
  25. 25. ACCOUNT HOLDER 2 Modeling a fraud ring as a graph ACCOUNT HOLDER 1 ACCOUNT HOLDER 3
  26. 26. 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
  27. 27. ACCOUNT HOLDER 2 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 Modeling a fraud ring as a graph
  28. 28. Credit Card Fraud
  29. 29. Ryan Boyd Developer Relations @ Neo4j Nav Mathur Sr. Director Global Solutions @ Neo4j
  30. 30. Example #1 “Credit Card Testing”
  31. 31. Manual skimming of an ATM Sophisticated Data Breaches Retrieval of Credit Card Information Rogue Merchant
  32. 32. 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
  33. 33. Example #2 “Fraud Origination and Assessing Loss Magnitude”
  34. 34. TxTx Tx TxTx Tx Tx TxTxTx TxJohn
  35. 35. Tx $2000 TxTx Tx Tx TxTxTxTx Tx Tx Computer Store John
  36. 36. Tx $2000 Tx Tx $25$10$4 TxTx Tx Tx TxTxTx Computer Store John Gas Station
  37. 37. 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 StoreTx $3
  38. 38. 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 StoreTx $3 Robert TxTxTx Tx TxTx TxTxTx Tx Tx
  39. 39. 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
  40. 40. How Neo4j fits in
  41. 41. 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
  42. 42. 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
  43. 43. 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
  44. 44. 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
  45. 45. Summary
  46. 46. We talked about… Today’s Fraudsters Examples of different types of Fraud: Fraud Rings Credit Card Testing Fraud Origination How Neo4j Fits in an Architecture
  47. 47. 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
  48. 48. Valuable Resources! neo4jsandbox.com https://neo4j.com/use-cases/fraud-detection/ neo4j.com/product Sandbox Fraud Detection Product
  49. 49. Q&A

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  • JavierRuizMartin1

    Mar. 31, 2017
  • DimkaG

    Apr. 3, 2017
  • yaqinov

    May. 10, 2017
  • HarishSehgal2

    Jun. 19, 2017

Financial Services firms are having difficulty with traditional fraud prevention measures that focus on discrete data points such as specific accounts, individuals, devices or IP addresses. However, today’s sophisticated fraudsters escape detection by using sophisticated techniques like card testing, masquerading as a legitimate merchant, skimming cards at vulnerable merchants, forming fraud rings comprised of stolen and synthetic identities, etc. To uncover such fraud rings, it is essential to look beyond individual data points to the connections that link them. Big Data Platforms and Data Science teams have been deployed to get rid of this menace but it takes weeks and months to uncover these patterns leading to high risk levels and inability to catch the fraudsters before they move on. Join this webinar to find out why enterprise organizations use Neo4j to augment their existing fraud detection capabilities to combat a variety of financial crimes – and doing so in real-time.

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