1.
Detecting eCommerce
fraud with Neo4j and
Linkurious.
SAS founded in 2013 in Paris | http://linkurio.us | @linkurious

2.
Agenda and speakers.
▪ Intro
▪ Graph technologies
▪ eCommerce Fraud
▪ Detection technical challenges
▪ Benefits of using Linkurious & Neo4j
▪ Demo
▪ How it works
▪ Use case
▪ Q&A
Mohsan is a Business Developer
at Linkurious. He works with
companies worldwide to help them find
solutions to uncover hidden insights in
their connected data.
“
”
Elise is a Marketing Project
Manager at Linkurious. She works
with Linkurious' partners to cover the
emerging graph technology use
cases.
“
”

3.
Graph visualization and analysis
startup founded in 2013.
200+ customers worldwide (NASA,
Cisco, French Ministry of Finances).
Linkurious Enterprise and Linkurious
SDK.
About Linkurious.

4.
Unlocking the value of graph data.
A graph is a set of data recorded as
entities (nodes) and relationships
(edges).
Graph databases like Neo4j store &
process large connected graphs in
real-time.
Linkurious’ software helps analysts
easily detect and investigate insights
hidden in graph data. Node
STORE
ACCESS
Data
Graph
database
Linkurious
ORGANIZE
Edge

5.
Typical use cases.
Cyber-security
Servers, switches, routers,
applications, etc.
Suspicious activity patterns,
identify impact of a compromised
asset.
IT Operations
Servers, switches, routers,
applications, etc.
Impact analysis, root cause
analysis.
Intelligence
People, emails, transactions,
phone call records, social.
Detecting and investigating
criminal or terrorist networks.
AML
People, transactions, watch-lists,
companies, organizations.
Detecting suspicious
transactions, identify beneficiary
owners.
Fraud
Claims, people, financial records,
personal data.
Detecting and investigating
criminal networks.
Life Sciences
Proteins, publications,
researchers, patents, topics.
Understanding protein
interactions, new drugs.
Enterprise
Architecture
Servers, applications, metadata,
business objectives.
Data lineage, curating enterprise
architecture.

6.
Fraud schemes that uses internet
related means (emails, websites, etc) to
present fraudulent solicitations to
prospective victims or to conduct
fraudulent transactions.

7.
A large number of fraud schemes.
Friendly fraud
Affiliate fraud
Account takeover
Identity theft
Reshipping fraud
Promotion abuse
Phishing
Merchant fraud

8.
A fertile ground for eCommerce fraud.
eCommerce merchants
loose 1.39% of revenue to
fraud in average, which
accounts for billions of
dollars worldwide.
Juniper, ”Online payment fraud
whitepaper 2016-2020”.
Organized networks
New technologies
eCommerce growth

9.
Using a five-layers approach to detect online fraud.
Endpoint
centric
Navigation
centric
Channel
centric
Cross-channel
centric
Entity link
analysis
Layer 1 Layer 2 Layer 3 Layer 4 Layer 5
Gartner Inc, “The conceptual model of a layered approach for fraud detection” in Market Guide, Online Fraud Detection: A Layered Approach

10.
Few systems react in real-time & block
transactions at the point of service.
Relational databases & siloed
products offer no cross-channel view.
Challenges with traditional fraud solutions.
Closed architecture makes it hard to
keep pace with new schemes.

11.
Cross-channel centric layer.
Combine it with external data sources
& improve risk assessment.
Get a cross-channel & cross-product
view of your client behaviors.
Model your data into a single graph
& add new type of data at anytime.

12.
Entity-link analysis
Uncover hidden relationships in your
connected data.
Identify fraud rings and suspicious
patterns.
Analyze activities and relationships
within a network of related entities.

13.
Demo: Detecting eCommerce
fraud with Linkurious and Neo4j.
- Modeling data into a graph
- Cross-channel overview of the data
- Investigation of entity relationships in Linkurious
- Suspicious pattern detection and analysis

14.
Modeling our data into a graph.

15.
Load data into Neo4j from multiple
data sources.
Windows / Linux / Mac, on-premise
or in the cloud, supports all modern
browsers.
Use Linkurious Enterprise
off-the-shelf interface or build your
custom application with Linkurious
SDK.
How it works.
Omnichannel data (transactional
data, behavior analytics, 3rd party
data, user devices data, etc...)
Synchronize
automatically
Neo4j Graph
database
Linkurious Enterprise
or custom application

16.
Background
Fraud and compliance team of an
international bank.
Problem
Increase of credit card fraud online
and difficulty to detect suspicious
cases.
Benefit
Detection of suspicious patterns
and fast investigation.
Online banking fraud detection.

17.
Questions?

18.
www.linkurio.us
contact@linkurio.us

19.
Sources and links.
Bibliography :
● DB-Engines. Knowledge Base of Relational and NoSQL Database Management Systems. Ranking
per Categories. Available: http://db-engines.com/en/ranking_categories [Avril 2017]
● eMarketer, “Worldwide Retail Ecommerce Sales: The eMarketer Forecast for 2016”. Available:
http://totalaccess.emarketer.com/Reports/Viewer.aspx?R=2001849&ecid=MX1371
● Juniper, Online payement whitepaper 2016-2020. Available :
http://www.experian.com/assets/decision-analytics/white-papers/juniper-research-online-paymen
t-fraud-wp-2016.pdf
● United states Department of Justice “19 People Indicted Following Investigations”. Available:
https://www.justice.gov/usao-dc/pr/19-people-indicted-following-investigations-international-frau
d-and-money-laundering
Images:
● Istock
● Growth icon by hans draiman from the Noun Project
● Cash flow icon by rflor from the Noun Project
● Dollar notification icon by Martin Lebreton from the Noun Project