4. NEO4j solves retail-related challenges for some
of the largest companies in the world
Adidas uses Neo4j to combine
content and product data into a
single, searchable graph database
which is used to create a
personalized customer experience
“We have many different silos, many
different data domains, and in order
to make sense out of our data, we
needed to bring those together and
make them useful for us,”
– Sokratis Kartelias, Adidas
eBay Now Tackles eCommerce
Delivery Service Routing with Neo4j
“We needed to rebuild when growth
and new features made our slowest
query longer than our fastest delivery
- 15 minutes! Neo4j gave us best
solution”
– Volker Pacher, eBay
Walmart uses Neo4j to give
customer best web experience
through relevant and personal
recommendations
“As the current market leader in
graph databases, and with
enterprise features for scalability
and availability, Neo4j is the right
choice to meet our demands”.
- Marcos Vada, Walmart
37. 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
41. 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
42. 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
44. Revolving Debt
Number of Accounts
Normal behavior
Fraud Detection With Connected Analysis
Fraudulent pattern
45. 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.
46. Modeling a fraud ring as a graph
ACCOUNT
HOLDER 2
ACCOUNT
HOLDER 1
ACCOUNT
HOLDER 3
47. 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
48. 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
51. 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
52. 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
53. • 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