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
9. 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
10. 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
12. Revolving Debt
Number of Accounts
Normal behavior
Fraud Detection With Connected Analysis
Fraudulent pattern
13. 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.
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
PHONE
NUMBER
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
17. 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
20. 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
21. 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
22. 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
23. • 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