Using graph technologies to fight fraud
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The document discusses the use of graph technologies to combat fraud, highlighting the limitations of traditional relational databases in modeling complex relationships. It emphasizes the increasing importance of graph databases for fraud detection, offering tools to visualize and analyze connections among data entities. Linkurious provides a graph visualization platform aimed at enhancing fraud detection capabilities and accelerating investigations.
Using graph technologies to fight fraud
- 1. Using graph technologies to fight fraud. SAS founded in 2013 in Paris | http://linkurio.us | @linkurious
- 2. French startup specialized in graph-visualization. CTO Web-scale archiving Université de Technologie de Compiègne CMO >5 years in consulting Sciences Po + Ecole de Guerre Economique Jean Villedieu Sébastien Heymann David Rapin CEO Created Gephi Phd in CS and complex systems from UPMC
- 3. What is a graph? Father Of Father Of Siblings
- 4. A graph is a set of nodes and relationships. This is a node This is a relationship Father Of Father Of Siblings
- 5. Why graphs are important?
- 6. Rise of complex and connected data. Increase in volume New processes, more transactions, more social devices, more devices, etc. Increase in connectedness Customers, products, processes and devices interact with each other.
- 7. Relational DBS are not good at relationships. ● Cannot model data and relationships without complexity; ● Performance degrades with number and levels of relationships, and database size; ● Adding new types of data and relationships requires schema redesign.
- 8. Graph DBs unlock connected data. ● Store graphs of billions of nodes and edges; ● Query the graph to find interesting patterns; ● Popularity of graph databases has increased 500% in the last 2 years; ● Recommendation, fraud detection, network management, cybersecurity, health...
- 9. Why does it work well for fraud?
- 10. Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Endpoint- centric Navigation- centric Analysis of users and their endpoints Account- centric Cross- channels Entity link analysis Analysis of navigation behavior and suspect patterns Analysis of anomaly behavior on a per-channel basis Analysis of anomaly behavior correlated on a cross-channel basis Analysis of relationships to detect organized collusion Network analysis offers a unique opportunity to identify the sophisticated fraudsters who work under the radar but in a coordinated fashion.
- 11. AML. Normal customer #1 Normal customer #2 In business with Suspicious individual Known criminal In business with In business with Are my customers in contact with criminals?
- 12. Conflict of interest. Are my vendors linked to my employees? New vendor Address Employee Registered address Phone number Registered phone Registered phone Registered address
- 13. Synthetic identity. Customer #1 Address Customer #2 Phone number Bank loan Bank loan Am I loaning money to people who don’t exist?
- 14. Swiss Leaks. Finding the real owners of $100 billion. Real owner Pseudo owner Offshore entity Swiss bank account Controls ControlsMarried
- 15. Stolen credit cards. Customer #1 Merchant #3 Customer #2 Merchant #2 Merchant #1 Merchant #4 Who is stealing credit cards? Normal TX Contested TX Contested TX Normal TX Contested TX Contested TX
- 16. First enterprise-ready graph visualization platform.
- 17. Improve your fraud detection system. Detect new fraud cases. Graph databases like Neo4j provides the ability to identify fraud patterns at scale. Faster fraud investigations. Linkurious provides a simple interface to investigate suspicious patterns.
- 18. How it works. Legacy DB DB On-premise server Web-browser
- 19. Pricing. Starter Enterprise Toolkit Search and explore your data Enterprise-ready security and collaboration Build or improve an existing web application Desktop Server Code Single user Mature organization Mature organization €990/user/year €1,990/user/year €30,000/product/year
- 21. Want to know more? ● Insider trading: https://linkurio.us/using-graphs-to-uncover-insider-trading-schemes/ ● Conflict of interest: https://linkurio.us/fraud-detection-identifying-conflicts-of-interest-with-graphs/ ● Synthetic identify: https://linkurio.us/how-to-detect-bank-loan-fraud-with-graphs-part-1/ and https://linkurio.us/how-to-detect-bank-loan-fraud-with-graphs-part-2/ ● Ecommerce fraud: https://linkurio.us/reshipping-scams-and-network-visualization/ ● Swiss Leaks: https://linkurio.us/how-the-icij-used-linkurious-to-reveal-the-secrets-hidden-in-the- swiss-leaks-data/ ● Fraud detection in retail: https://linkurio.us/fraud-detection-in-retail/ ● Credit card fraud: https://linkurio.us/stolen-credit-cards-and-fraud-detection-with-neo4j/