graphday stockholm - levaraging graph-technology to fight financial fraud
TRANSCRIPT
LEVERAGING GRAPH-TECHNOLOGY TO FIGHT FINANCIAL FRAUD
Feb 2017
Stefan Kolmar
Director Field Engineering
Retail Banking First-Party Fraud!Opening many lines of credit with no intention of !
paying them back!
CausingHighImpact• TensofbillionsofdollarslosteveryyearbyU.S.Banks.(1)
• 25%oftotalconsumercreditcharge-offsintheUnitedStates.(2)
• 10%to20%ofunsecuredbaddebtatleadingU.S.andEuropeanbanksismisclassified,andisactuallyfirst-partyfraud.(3)
(1) Experian: http://www.experian.com/assets/decision-analytics/white-papers/first-partyfraud-wp.pdf!(2) Experian: http://www.experian.com/assets/decision-analytics/white-papers/first-partyfraud-wp.pdf!(3) Business Insider: http://www.businessinsider.com/how-to-use-social-networks-in-the-fight-against-first-party-fraud-2011-3!
Detec%ngFraudRings
SSN1!
123 NW 1st Street!San
Francisco, CA!
555-555-5555!
123 NW 1st Street!San
Francisco, CA!555-555-
5555!
Skimming
Person A! Person B!
Location A! Location B!
PhoneNumberDuplicateUse
555-555-5555!
Person A!
Person B!
SuspecteCommerce
Person A!
Person B!
Location C!IP address!
Fraud Demo – Part I (generic)!• Fraud scenario covering Retail Fraud use cases!
• Data set contains operational data!• Constant data load –> injecting fraud cases -> generate alerts!• Capability to export data of detected fraud for further investigation!
Neo4j!
App Server!
Fraud Detection!Web App! Fraud App!
Browser!
UX: TestDataG
en!
Alert generated!
Demo!
Why using GraphDB / Neo4j for Fraud Detection?!
• Graphs are intuitive to understand!• Schema free - > Flexibility!
• Nodes can vary depending on time / usage / semantic!• Adopt dynamic changes!
• Agile Development!• High productivity and rapid implementation !• No “RDBMS-waterfall-high-investment-trap” !
• Taking advantage of the full value of connected data and data relationships!• Traversing the graph compared to self joins in RDBMS!
• Near real time response times!• Preventing fraud rather than detecting after the fact!
• Usage scenario Fraud Analyst: !• Potential fraud case detected!• Enriched with data from various sources containing data on fraud suspect!• Trigger human and/or automated reactions!
FraudDemo–PartII
Neo4j!
Web App!
RDBMS!(Oracle, MySQL, DB2, HANA …)!
Management Console!(E.g BI Tools such as !
Tableau, Qlik, BO, MicroStrategy etc)!
FraudAnalyst
Machine2Machine !generated actions!
Alert!
Incoming Events!
CRM System!
!!!!!
Operational System!!!
Data!Integration!
External Data!
Using Neo as the foundation of a fraud solution in your architecture!
Step 1: Set up Data Integration!Step 2: Visualize Data in BI Tool!
Conclusions!• Fraud as one use case to provide full value of connected data within the
entire organization!
• Neo4j as the foundation to do 360 degree fraud detection and prevention!
• Neo4j to extend your existing environment while protecting your investments!
• Neo4j provides best value integrated in the entire environment!
• Neo4j as the foundation for generating real time alerts to trigger automated or manual interventions!
!
A deeper look into the database!
A brief look into the data model ….!
THANK YOU!