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GraphTalks Copenhagen - Analyzing Fraud with Graph Databases
This document discusses how graph databases can help detect fraud. It outlines traditional discrete fraud detection methods and their weaknesses in detecting complex fraud schemes. A graph database approach allows for connected analysis across entities and relationships to better identify organized fraud, stolen identities, and other difficult to detect patterns. The document provides examples of insurance fraud detection using a graph database to visualize related individuals, vehicles, businesses and events.
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GraphTalks Copenhagen - Analyzing Fraud with Graph Databases
- 1. Analyzing Fraud with GraphDatabases
- 2. What Does FraudLook Like?
- 3. Organized in groupsSynthetic Identities Stolen Identities Hijacked Devices What Does Fraud Look Like?
- 4. Types of Fraud •Insurance Fraud • eCommerce Fraud • Credit Card Fraud • Rogue Merchants • Fraud we don’t know about yet…
- 5. Endpoint-Centric Analysis of usersand 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
- 6. DISCRETE ANALYSIS Endpoint-Centric Analysis ofusers 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
- 7. INVESTIGATE Revolving Debt Number ofAccounts INVESTIGATE Normal behavior Fraud Detection with Discrete Analysis
- 8. Difficult or unableto detect: • Synthetic identities • Stolen identities • Fraud rings • Nth degree links • 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. CONNECTED ANALYSIS Endpoint-Centric Analysis ofusers 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. Modern Fraud Detection
- 10. Revolving Debt Number ofAccounts Normal behavior Fraudulent pattern Fraud Detection with Connected Analysis
- 11. ACCOUNT HOLDER 2 ACCOUNT HOLDER 1 ACCOUNT HOLDER3 Fraud Detection with Connected Analysis
- 12. ACCOUNT HOLDER 2 ACCOUNT HOLDER 1 ACCOUNT HOLDER3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT PHONE NUMBER UNSECURED LOAN SSN 2 UNSECURED LOAN Fraud Detection with Connected Analysis
- 13. ACCOUNT HOLDER 2 ACCOUNT HOLDER 1 ACCOUNT HOLDER3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT ADDRESS PHONE NUMBER PHONE NUMBER SSN 2 UNSECURED LOAN SSN 2 UNSECURED LOAN Fraud Detection with Connected Analysis
- 14. CONNECTED ANALYSIS Endpoint-Centric Analysis ofusers 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. Modern Fraud Detection
- 15. “Don’t consider traditional technologyadequate to keep up with criminal trends” Market Guide for Online Fraud Detection, April 27, 2015
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- 17. Money Transferring Purchases Bank Services Relational/tabular database DevelopBatch Jobs Data Scientists + Good for Discrete Analysis – No Holistic View of Data-Relationships – Slow query speed for connections Insurance Claims
- 18. Relational database Data Lake + Goodfor Map Reduce + Good for Analytical Workloads – No holistic view – Non-operational workloads – Weeks-to-months processes Develop Patterns Data Scientists Merchant Data Credit Score Data Other 3rd Party Data Money Transferring Purchases Bank Services Insurance Claims
- 19. Data Lake Neo4j powers 360°view of transactions in real-time SENSE Transaction stream RESPOND Alerts & notification SYNC RELEVANT DATA Relational database Visualization UI Fine Tune Patterns Develop Patterns Data Scientists Merchant Data Credit Score Data Other 3rd Party Data Money Transferring Purchases Bank Services Insurance Claims Neo4j Cluster
- 20. Data Lake Neo4j powers 360°view of transactions in real-time SENSE Transaction stream RESPOND Alerts & notification SYNC RELEVANT DATA Relational database Visualization UI Fine Tune Patterns Develop Patterns Data Scientists Merchant Data Credit Score Data Other 3rd Party Data Money Transferring Purchases Bank Services Insurance Claims Neo4j Cluster New perspective into business
- 21. Data Lake Neo4j powers 360°view of transactions in real-time SENSE Transaction stream RESPOND Alerts & notification SYNC RELEVANT DATA Relational database Visualization UI Fine Tune Patterns Develop Patterns Data Scientists Merchant Data Credit Score Data Other 3rd Party Data Money Transferring Purchases Bank Services Insurance Claims Neo4j Cluster Rule-based scoring Predictive analytics Case management Augments classic tools:
- 22. The Impact ofFraud 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
- 23. Paper Collisions Insurance scammersinvent automobile accidents complete with fake drivers, passengers and witnesses Insurance Fraud Example
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- 29. KDD 2015 3 Subgraphs Subsetof nodes and relationships in the data
- 30. Ego networks E The subgraphmade up of all neighbors and the relationships among them Generalizable to Nth degree neighbors
- 31. Same Data, DifferentPerspective
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- 39. MATCH (u1:User {name:”Rik”})––(x)––(u2:User) WHEREu1 <> u2 AND NOT (x:IP) RETURN x Network in common is OK Sample Query
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- 41. Detect & preventfraud 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
- 42. Don’t be alonely node. Connect with us :-)