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GraphDay Stockholm - Fraud Prevention


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David Montag, Neo Technology
GraphDay Stockholm

Published in: Technology
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GraphDay Stockholm - Fraud Prevention

  1. 1. Fraud Prevention A practical example of graph databases in action David Montag
  2. 2. A Graph Is Connected Data
  3. 3. Neo4j solves challenges for some of the most powerful companies in the world Adidas uses Neo4j to combine content and product data into a single, searchable graph database which is used to create a personalized customer experience “We have many different silos, many different data domains, and in order to make sense out of our data, we needed to bring those together and make them useful for us,” 
 – Sokratis Kartelias, Adidas eBay Now Tackles eCommerce Delivery Service Routing with Neo4j “We needed to rebuild when growth and new features made our slowest query longer than our fastest delivery - 15 minutes! Neo4j gave us best solution” 
 – Volker Pacher, eBay Walmart uses Neo4j to give customer best web experience through relevant and personal recommendations “As the current market leader in graph databases, and with enterprise features for scalability and availability, Neo4j is the right choice to meet our demands”. 
 - Marcos Vada, Walmart
  4. 4. This shift is happening! Why?
  5. 5. Operational Databases Applications X Pre-computed Queries (Data Warehouse / RDBMS) Real-time & Dynamic Queries
 (Graph Database) Why Graphs Now? We need something that is: • Flexible to change • Scalable to many problems • Intuitive to understand • Instantly responsive
  6. 6. Main Strengths Main Risks • Widespread competence and familiarity • Integration with wide range of tools • “Analytical questions in transactional time” • Flexible and scalable to future needs • New tools require new competence • Misunderstandings can result in tool being wrongly applied • Incapable of meeting flexibility and performance demands • Lacks game changer success stories — status quo is risky in disruptive times Ways of Thinking About Data Pre-computed Queries (Data Warehouse / RDBMS) Real-time & Dynamic Queries
 (Graph Database)
  7. 7. A Graph Example: Fraud Prevention
  8. 8. Fraud Detection & Prevention Types of Fraud • Retail Banking Fraud • Insurance Fraud Identity types • Stolen • Fake • Synthetic Types of Analysis • Discrete • Connected
  9. 9. Retail Banking First-Party Fraud Opening many lines of credit with no intention of paying them back
  10. 10. • 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) First-Party Fraud Impact (1) Experian: (2) Experian: (3) Business Insider:
  11. 11. Three Kinds of Identities, Fraud Rings 145 Hickory Rd
 Pasadena, CA 415 Hickory St
 Pasadena, CA 626-407-1234 626-814-6532
  12. 12. Gartner’s Layered Fraud Prevention Approach (4) (4) Traditional Fraud Prevention Analysis of users 
 and their endpoints Analysis of
 navigation behavior and suspect patterns Analysis of anomaly behavior by channel Analysis of anomaly behavior correlated across channels Analysis of relationships to detect organized crime and collusion Layer 1 Endpoint-
 Centric Navigation-
 Centric Account-
 Centric Cross-
 Channel Entity 
  13. 13. Pros
 Stops rookies Discrete Data Analysis Revolving
 Debt INVESTIGATE INVESTIGATE Number of accounts Cons
 False positives
 False negatives
  14. 14. Connected Analysis Revolving
 Debt Number of accounts PROS
 Detect fraud rings
 Fewer false negatives
  15. 15. Value
 Effective in detecting some of the most impactful attacks, even from organized rings Challenge
 Extremely difficult with traditional technologies For example a ten-person fraud bust-out is $1.5M, assuming 100 false identities 
 and 3 financial instruments per identity, each with a $5K credit limit Connected Analysis with Neo4j
  16. 16. Demo
  17. 17. Insurance Fraud ”Whiplash for Cash” Paper Collisions Insurance scammers invent automobile
 accidents complete with fake drivers,
 passengers and witnesses
  18. 18. Whiplash for Cash Example Accidents Cars Doctor Attorney People Drives Is Passenger Drivers
  19. 19. View of fraud ring 
 in a graph database Modeling Insurance Fraud as a Graph Accident
 1 Accident
 2 Person
 1 Person
 2 Person
 3 Person
 4 Person
 5 Person
 6 Car
 1 Car
 2 Car
 3 Car
  20. 20. Doing Connected Analysis is Challenging • Large amounts of data and relationships must be processed • New data and relationships are continually being added • Fraud rings must be uncovered in 
 real-time to prevent fraud
  21. 21. Adopting a Pattern-oriented Mindset Search-oriented • Good when you know exactly what you’re looking for • Primarily based on explicit search criteria Pattern-oriented • Good when you want to suggest what might fit • Primarily based on implicit information, often many “hops” away
  22. 22. The Case For Innovation with Graphs During past 20 years, society has become hyperconnected. We considered how regular people tend to think and reason, and modeled Neo4j to match that. Neo4j allows you to naturally map together the data that matters to you in a graph — like a mind map! … Graph structure scales to many problems, and is highly flexible to change. … Unlock the business value of connections and relationships in data. … How can we discover insights hidden behind the complexity? … Traditional technology does not handle complexity well. … Information-wise, gone from a small town to a metropolis. Complexity has exploded. … Need for better insights driven by competition and disruption. There is a shift happening now. The world’s largest companies rely on Neo4j. The competitive advantage is real. … In uncertain times, many consider the risks of change, but what are the risks of not adapting? … Get in the driver’s seat. Be the bringer of innovation. … The Problem The Solution The Future
  23. 23. THANK YOU! (me)-[:ASKS_FOR]->(tweet {say: “Neo4j GraphDay: what a time to be alive!”})-[:FROM]->(you)