201411203 goto night on graphs for fraud detection
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This document discusses how graph databases can be useful for fraud detection. It begins with an introduction to graphs and graph theory, then discusses how graph databases work and their advantages over relational databases for complex querying and modeling connected data. The document notes that fraud detection relies on real-time analysis, complex patterns, and graph algorithms to navigate relationships. It provides a short demonstration and discusses case studies where graph databases have been successfully used for fraud detection due to their ability to efficiently handle large, interconnected datasets.
201411203 goto night on graphs for fraud detection
- 1. Graphs for Fraud Detec1on rik@neotechnology.com @rvanbruggen
- 2. Agenda • About Graphs • About Graph Databases • Why Graph Databases ma<er for Fraud Detec?on – Short demonstra?on • Case Studies • Q&A
- 5. Meet Leonhard Euler • Swiss mathema?cian • Inventor of Graph Theory (1736)
- 6. Königsberg (Prussia) -‐ 1736
- 7. A B D C
- 8. A B D C 1 2 3 4 7 6 5
- 10. So what is a graph database? • OLTP database – “end-‐user” transac?ons • Model, store, manage data as a graph
- 11. What is a graph? Node Rela?onship
- 12. Contrast with Rela?onal Graphs are often referred to as “Whiteboard Friendly”. The data model reflects the way a domain expert would naturally draw their data on a whiteboard “The schema is the data”. Schema flexibility allows the system to change in response to a changing environment
- 13. What are graphs good for? Complex Querying
- 14. Examples of complex queries? 1. Semi-‐structure in datasets 1 – Normaliza?on introduces complexity – Forces developers to develop all kinds of logic to deal with this variability in their applica?on logic
- 15. Examples of complex queries: 2. Connectedness in data Lots of normalized rela?onships between the different en??es, forces developers to do • Deep joins • Recursive joins • Pathfinding opera?ons • “open-‐ended” queries
- 17. Graphs in Fraud Detec1on?
- 18. Recommender Fraud Detec?on systems
- 19. Recommender Fraud Detec?on systems Fraud Detection
- 20. Graphs in Fraud Detec?on Systems • Real ?me aspect • Detec?on Prac?ces rely on Graph Algorithms • Opera?onal efficiency
- 21. Real 1me fraud detec?on? • Context is everything – You don’t want to be blocking credit cards for no reason… false posi?ves are fatal… • Complexity maers – Need to outsmart the “bad guys” – Assume that they can and will understand/beat the system • Visualiza?on maers – Manual interven?on relies on fast understanding of context – and visualiza?on helps there
- 22. Fraud Detec?on prac?ces: Graph Algorithms ● Helpful for naviga?ng complex networks ● tell me how A and B are related ● The things on the path between A and B could very well be interes?ng ● ShortestPath, AllShortestPaths, Weighted ShortestPath (Dijkstra, A*) ● Helpful for understanding the important parts of a network ● Clusters ● Bridges ● Centrality ● Betweenness Centrality ● (Page)Ranking
- 23. Opera1onal Efficiency • Graph datamodel removes the need for many “batch opera?ons” – No need to precalculate – just feed it into the graph • Complex paern matching in milliseconds • Graph Locality == Predictability Speed, even over large datasets
- 24. Short demo -‐ 1 ● Circular paerns omen indicate some kind of aempt to “trick the system”
- 25. Short demo -‐ 2
- 29. Neo4j versions / licenses Neo4j License Overview Developer! Seats! Personal Startup / Departmental Enterprise deployment models ($6K*/Developer/Year) Test! Instances! ($6K/Instance/Year) Production! Instances! (Bundle / Core Pricing) Open source Commercial license terms available Specific OEM models Instances whose purpose is to ensure that the software accessing Neo4j is meeting specification.! ! (e.g. System Test, Integration Test, UAT, Performance Test, Staging) Instances that store and process data in a way that benefits and advances an organization’s goals.! ! May be accessed by applications and/or end users Includes access by programmers to licensed test instances, and private instances on the programmer’s personal machine for the sole purpose of writing, debugging, or testing software designed to access Neo4j *Or otherwise, depending on the Bundle, and negotiation
- 30. Future trainings events! 3
- 31. QA, Conclusion, Next Steps Neo Technology www.neotechnology.com Neo4j www.neo4j.org rik@neotechnology.com or +32 478 686800