2. AGENDA
• Meet today’s fraudsters
• Traditional fraud detection methods
• Using connected analysis for real-time fraud detection
• Demo
• Summary
3.
4. The Impact of Fraud
The payment card fraud alone,
constitutes for over 16 billion dollar in
losses for the bank-sector in the US.
$16Bpayment card fraud in 2014*
Banking
$32Byearly e-commerce fraud**
Fraud in E-commerce is estimated
to cost over 32 billion dollars
annually is the US..
E-commerce
The impact of fraud on the insurance
industry is estimated to be $80
billion annually in the US.
Insurance
$80Bestimated yearly impact***
*) Business Wire: http://www.businesswire.com/news/home/20150804007054/en/Global-Card-Fraud-Losses-Reach-16.31-Billion#.VcJZlvlVhBc
**) E-commerce expert Andreas Thim, Klarna, 2015
***) Coalition against insurance fraud: http://www.insurancefraud.org/article.htm?RecID=3274#.UnWuZ5E7ROA
8. Endpoint-Centric
Analysis of users and
their end-points!
1.
Navigation Centric
Analysis of
navigation behavior
and suspect
patterns!
2.
Account-Centric
Analysis of anomaly
behavior by channel!
3.
PC:s
Mobile Phones
IP-addresses
User ID:s
Comparing Transaction
Identity Vetting
Traditional Fraud Detection Methods
9. !
Unable to detect!
• Fraud rings!
• Fake IP-adresses!
• Hijacked devices!
• Synthetic Identities!
• Stolen Identities!
• And more…!
Weaknesses
DISCRETE ANALYSIS
Endpoint-Centric
Analysis of users and
their end-points!
1.
Navigation Centric
Analysis of
navigation behavior
and suspect
patterns!
2.
Account-Centric
Analysis of anomaly
behavior by channel!
3.
Traditional Fraud Detection Methods
11. Revolving Debt!
Number of Accounts!
Normal behavior
Fraud Detection With Connected Analysis
Fraudulent pattern
12. CONNECTED ANALYSIS
Augmented Fraud Detection
Endpoint-Centric
Analysis of users and
their end-points!
Navigation Centric
Analysis of
navigation behavior
and suspect
patterns!
Account-Centric
Analysis of anomaly
behavior by channel!
DISCRETE ANALYSIS
1.
2.
3.
Cross Channel
Analysis of anomaly
behavior correlated
across channels!
4.
Entity Linking
Analysis of relationships
to detect organized
crime and collusion!
5.
14. ACCOUNT
HOLDER 2
Modeling a fraud ring as a graph
ACCOUNT
HOLDER 1
ACCOUNT
HOLDER 3
CREDIT
CARD
BANK
ACCOUNT
BANK
ACCOUNT
BANK
ACCOUNT
PHONE
NUMBER
UNSECURED
LOAN
SSN 2
UNSECURED
LOAN
15. ACCOUNT
HOLDER 2
Modeling a fraud ring as a graph
ACCOUNT
HOLDER 1
ACCOUNT
HOLDER 3
CREDIT
CARD
BANK
ACCOUNT
BANK
ACCOUNT
BANK
ACCOUNT
ADDRESS
PHONE
NUMBER
PHONE
NUMBER
SSN 2
UNSECURED
LOAN
SSN 2
UNSECURED
LOAN
16. ACCOUNT
HOLDER 2
Modeling a fraud ring as a graph
ACCOUNT
HOLDER 1
ACCOUNT
HOLDER 3
CREDIT
CARD
BANK
ACCOUNT
BANK
ACCOUNT
BANK
ACCOUNT
ADDRESS
PHONE
NUMBER
PHONE
NUMBER
SSN 2
UNSECURED
LOAN
SSN 2
UNSECURED
LOAN
SYNTETIC
PERSON 2
SYNTHETIC
PERSON 1
19. Account-Centric
Analysis of anomaly
behavior correlated
across channels!
4.
Entity Linking
Analysis of relationships
to detect organized
crime and collusion!
5.
CONNECTED ANALYSIS
Endpoint-Centric
Analysis of users and
their end-points!
Navigation Centric
Analysis of
navigation behavior
and suspect
patterns!
Account-Centric
Analysis of anomaly
behavior by channel!
DISCRETE ANALYSIS
1.
2.
3.
Augment Fraud Detection with Neo4j
Traditional Vendors
20. ACCEPT / DECLINE
MANUAL
User/Transaction!
CONNECTED ANALYSIS
User/Transaction!
ACCEPT / DECLINE(DISCRETE ANALYSIS)
+
User/Transaction! (sub-second performance to
any data size and connection)!
ACCEPT / DECLINE
REAL TIME
TRADITIONAL VENDORS (DISCRETE ANALYSIS)
(DISCRETE ANALYSIS)
ACCEPT / DECLINE
How Neo4j fits in
21. Detect & prevent fraud in real-time!
Faster credit risk analysis and transactions!
Reduce chargebacks!
Quickly adapt to new methods of fraud!
Why Neo4j?! Who’s using it?!
Financial institutions use Neo4j to:
FINANCE
Government Online Retail
22. • Today’s fraudsters are organized and highly sophisticated
• Legacy technology does not detect fraud sufficiently and in real-time
• Graph-databases enable you to discover fraudulent patterns in real-
time
• Augment your current fraud detection infrastructure with connected
analysis
KEY TAKE AWAYS
24. 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!
26. 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!
28. 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!
29. • 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!
30. 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!
31. 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!
!