How to apply graph analytics for bank loan fraud detection?


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A walk through a common fraud case : how to use Neo4j and graph visualization to identify criminals and fight loan fraud.

Published in: Data & Analytics, Technology

How to apply graph analytics for bank loan fraud detection?

  1. 1. How to apply graph analytics for bank loan fraud detection? SAS founded in 2013 in Paris | | @linkurious
  2. 2. WHAT IS A GRAPH? Father Of Father Of Siblings This is a graph
  3. 3. WHAT IS A GRAPH : NODES AND RELATIONSHIPS Father Of Father Of Siblings A graph is a set of nodes linked by relationships This is a node This is a relationship
  4. 4. People, objects, movies, restaurants, music Antennas, servers, phones, people Supplier, roads, warehouses, products Graphs can be used to model many domains DIFFERENT DOMAINS WHERE GRAPHS ARE IMPORTANT Supply chains Social networks Communications
  5. 5. But why can graphs can help identify fraud? GRAPH AND FRAUD DETECTION
  6. 6. AITE Group estimates that first party fraud will cost $28.6 billion in credit card losses a year by 2016. THE COST OF FRAUD
  7. 7. A look at a common fraud scenario banks face A COMMON FRAUD SCENARIO Create a fake identity Go to the bank, ask for a loan Disappear with the money A criminal uses the fake identity to register a bank account. He acts like a normal customer and tries to secure a loan Once the criminal feels he cannot get access to more money he carefully prepares his exit : in a short amount of time he empties all of his accounts and disappears A criminal or a group of criminal mix pieces of information (addresses, phone numbers, social security number) to create a “synthetic-identity”
  8. 8. THE PAINS OF WORKING ON CONNECTED DATA WITH RELATIONAL TECHNOLOGIES Relational databases are not good at handling... relationships Depth RDBMS execution time (s) Neo4j execution time (s) Records returned 2 0.016 0.01 ~2500 3 30.267 0.168 ~110 000 4 1543.505 1.359 ~600 000 5 Unfinished 2.132 ~800 000 Finding extended friends in a 1M people social network (from the book Graph Databases)
  9. 9. Loan $25k Home address 58, Eisenhower Square A GRAPH DATA MODEL FOR FRAUD DETECTION Customer name J. Smith Phone number +33 5 68 98 25 74 The first step to detect fake identity is to use a graph to model customer information Credit card 1 234$ ID J. Smith A graph showing a legitimate customer and the information she is linked to
  10. 10. In a fraud ring people share the same information A LOOK AT A FRAUD RING THROUGH GRAPH VISUALIZATION 58, Eisenhower Square 14, Roses Street +33 6 75 89 22 14 $7k P. Martin $12,5k +331 42 58 66 00 J. Smith SSN 17873897893 8, Sugar Hill Street $20k E. Selmati SSN 1787576553 $45k P. Smith SSN 1787579953 SSN 1267576553 8, Coronation Street
  11. 11. HOW TO APPLY IT IN THE REAL WORLD Graph databases makes it possible to identify the fraud patterns in real-time Lifecycle events trigger security checks A new customer opens an account An existing customer asks for a loan A customer skips a loan payment A Neo4j Cypher query runs to detect patterns The bank can make an informed decision
  12. 12. The fraud teams acts faster and more fraud cases can be avoided. WHAT IS THE IMPACT OF LINKURIOUS If something suspicious comes up, the analysts can use Linkurious to quickly assess the situation Linkurious allows the fraud teams to go deep in the data and build cases against fraud rings. Treat false positives Investigate serious cases Save money Linkurious allows you to control the alerts and make sure your customers are not treated like criminals.
  13. 13. DEMO Go to to try it!
  14. 14. TECHNOLOGY Cloud ready and open-source based
  15. 15. OTHER USE CASES Graphs are everywhere, learn to leverage them
  16. 16. CONCLUSION Contact us to discuss your projects at
  17. 17. Detailed use case on our blog : ● Part 1 : ● Part 2 : ● Neo4j data set : GraphGist by Kenny Bastani : 2FBankFraudDetection.adoc Video demonstration : (around the 12 minutes mark) ADDITIONAL RESOURCES