Watch this webinar: https://www.youtube.com/watch?v=OApN-I7k44c&feature=youtu.be

1. Fraud Demo
Solu%ons powered with Neo4j
2015
Stefan Kolmar
Neo Technologies

2. Retail Banking First-Party Fraud
Opening many lines of credit with no inten5on 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: hQp://www.experian.com/assets/decision-analy5cs/white-papers/first-partyfraud-wp.pdf
(2) Experian: hQp://www.experian.com/assets/decision-analy5cs/white-papers/first-partyfraud-wp.pdf
(3) Business Insider: hQp://www.businessinsider.com/how-to-use-social-networks-in-the-fight-against-first-party-fraud-2011-3

3. Detec%ng Fraud Rings
SSN1
123 NW
1st Street
San
Francisco,
CA
555-5
55-55
55
123 NW
1st Street
San
Francisco,
CA 555-5
55-55
55
Skimming
Person A Person B
Loca5on A Loca5on B
Phone Number Duplicate Use
555-555-5
555
Person A
Person B
Suspect eCommerce
Person A
Person B
Loca5on C
IP address

4. Fraud Demo – Part I (generic)
• Fraud scenario covering Retail Fraud use cases
• Data set contains opera5onal data
• Constant data load –> injec5ng fraud cases -> generate alerts
• Capability to export data of detected fraud for further inves5ga5on
Neo4j
App Server
Fraud
Detec5on
Web App
Fraud App
Browser
UX:
TestDataGen
Alert generated

5. Demo

6. Why using GraphDB / Neo4j for Fraud?
• Agile Development
• High produc5vity and rapid implementa5on
• No “RDBMS-waterfall-high-investment-trap”
• Taking advantage of the full value of connected data
• Traversing the graph compared to self joins in RDBMS
• Near real 5me response 5mes
• Preven5ng fraud rather than detec5ng ader the fact
• Schema free
• Nodes can vary depending on 5me / usage / seman5c

7. • Usage scenario Fraud Analyst:
• Poten5al fraud case detected
• Enriched with data from various sources containing data on fraud suspect
• Trigger human and/or automated reac5ons
Fraud Demo – Part II
Neo4j
Web
App Data
Integra5on
RDBMS
(Oracle, MySQL,
DB2, HANA …)
Management Console
(E.g BI Tools such as
Tableau, Qlik, BO,
MicroStrategy etc)
Fraud
Analyst
Machine2Machine
generated ac5ons
Alert
Incoming Events
CRM System
Opera5onal System
External Data

8. Using Neo as the founda%on of a fraud
solu%on in your architecture
Step 1: Set up Data Integra5on
Step 2: Visualize Data in BI Tool

9. Conclusions
• Fraud as one use case to provide full value of connected data within
the en5re organiza5on
• Neo4j as the founda5on to do 360 degree fraud detec5on and
preven5on
• Neo4j to extend your exis5ng environment while protec5ng your
investments
• Neo4j provides best value integrated in the en5re environment
• Neo4j as the founda5on for genera5ng real 5me alerts to trigger
automated or manual interven5ons

10. Encore

11. … One more thing ...

12. A brief look into the data model ….

13. Fraud Demo
Solu%ons powered with Neo4j
2015
Stefan.Kolmar@neotechnology.com