GraphDay Stockholm - Levaraging Graph-Technology to fight Financial Fraud

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1) Graph technology can be leveraged to fight financial fraud by detecting fraud rings and relationships between entities like individuals and locations that may indicate fraudulent activity. 2) A graph database like Neo4j is well suited for fraud detection because it can easily model and traverse complex relationships in connected data, identify patterns more quickly than SQL, and enable near real-time response. 3) A fraud detection demo using Neo4j showed how operational data from various sources could be integrated and analyzed to generate alerts when potential fraud cases are detected.

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!
Causing High Impact
•  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)

(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%ng Fraud Rings
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!
Phone Number Duplicate Use
555-555-5
555!
Person A!
Person B!
Suspect eCommerce
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!

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!
Fraud Demo – Part II
Neo4j!
Web App!
RDBMS!
(Oracle, MySQL,
DB2, HANA …)!
Management Console!
(E.g BI Tools such as !
Tableau, Qlik, BO,
MicroStrategy etc)!
Fraud
Analyst
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!
!

Fraud Demo!
Solutions powered with Neo4j !
2017!
!
Stefan.Kolmar@neotechnology.com!

Investigating fraud often involves identifying suspicious patterns among mountains of uninteresting transactional data. A new partnership between Neo Technologies and Cambridge Intelligence allows fraud investigators and data analysts to uncover these patters far more easily. By combining the power of Neo4j's graph database and the visualization capabilities of KeyLines, a web-based graph visualization engine tightly integrated with Neo4j's data model, these investigators and analysts can visually drill down from aggregate data to the individual suspicious data elements quickly and without requiring significant technical expertise in query languages. This presentation will summarize the Neo Technology and Cambridge Intelligence partnership, discuss the technical integration between the two products, and demonstrate a number of different scenarios of uncovering fraud across multiple domains and data types.

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