David Montag, Neo Technology GraphDay Stockholm 25.2.2016

1. Fraud Prevention
A practical example of graph databases in action
David Montag
david@neotechnology.com

2. A Graph Is Connected Data

4. Neo4j solves challenges for some of the
most powerful 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

5. This shift is happening!
Why?

6. Operational
Databases
Applications
X
Pre-computed Queries
(Data Warehouse / RDBMS)
Real-time & Dynamic Queries
(Graph Database)
Why Graphs Now?
We need something that is:
• Flexible to change
• Scalable to many problems
• Intuitive to understand
• Instantly responsive

7. Main
Strengths
Main
Risks
• Widespread competence and familiarity
• Integration with wide range of tools
• “Analytical questions in transactional time”
• Flexible and scalable to future needs
• New tools require new competence
• Misunderstandings can result in tool
being wrongly applied
• Incapable of meeting flexibility and
performance demands
• Lacks game changer success stories —
status quo is risky in disruptive times
Ways of Thinking About Data
Pre-computed Queries
(Data Warehouse / RDBMS)
Real-time & Dynamic Queries
(Graph Database)

8. A Graph Example:
Fraud Prevention

9. Fraud Detection & Prevention
Types of Fraud
• Retail Banking Fraud
• Insurance Fraud
Identity types
• Stolen
• Fake
• Synthetic
Types of Analysis
• Discrete
• Connected

10. Retail Banking First-Party Fraud
Opening many lines of credit with no intention of paying them back

11. • 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)
First-Party Fraud Impact
(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

12. Three Kinds of Identities, Fraud Rings
145 Hickory Rd
Pasadena, CA
415 Hickory St
Pasadena, CA
626-407-1234
626-814-6532

13. Gartner’s Layered Fraud Prevention Approach (4)
(4) http://www.gartner.com/newsroom/id/1695014
Traditional Fraud Prevention
Analysis
of users
and their
endpoints
Analysis of
navigation
behavior and
suspect patterns
Analysis of
anomaly
behavior by
channel
Analysis of
anomaly
behavior
correlated across
channels
Analysis of
relationships
to detect
organized crime
and collusion
Layer 1
Endpoint-
Centric
Navigation-
Centric
Account-
Centric
Cross-
Channel
Entity
Linking
Layer 2 Layer 3 Layer 4 Layer 5
DISCRETE DATA ANALYSIS CONNECTED ANALYSIS

14. Pros
Simple
Stops rookies
Discrete Data Analysis
Revolving
Debt
INVESTIGATE
INVESTIGATE
Number of accounts
Cons
False positives
False negatives

15. Connected Analysis
Revolving
Debt
Number of accounts
PROS
Detect fraud rings
Fewer false negatives

16. Value
Effective in detecting some of the
most impactful attacks, even from
organized rings
Challenge
Extremely difficult with traditional
technologies
For example a ten-person fraud bust-out is $1.5M, assuming 100 false identities
and 3 financial instruments per identity, each with a $5K credit limit
Connected Analysis with Neo4j

17. Demo

18. Insurance Fraud ”Whiplash for Cash”
Paper Collisions
Insurance scammers invent automobile
accidents complete with fake drivers,
passengers and witnesses

20. Whiplash for Cash Example
Accidents
Cars
Doctor Attorney
People
Drives
Is Passenger
Drivers
Passengers
Witnesses

21. View of fraud ring
in a graph database
Modeling Insurance Fraud as a Graph
Accident
1
Accident
2
Person
1
Person
2
Person
3
Person
4
Person
5
Person
6
Car
1
Car
2
Car
3
Car
4
INVOLVES
DRIVES
REPRESENTS
WITNESSES
ADJUSTS
HEALS

22. Doing Connected Analysis is Challenging
• Large amounts of data and relationships
must be processed
• New data and relationships are continually
being added
• Fraud rings must be uncovered in
real-time to prevent fraud

23. Adopting a Pattern-oriented Mindset
Search-oriented
• Good when you know
exactly what you’re
looking for
• Primarily based on explicit
search criteria
Pattern-oriented
• Good when you want to
suggest what might fit
• Primarily based on implicit
information, often many
“hops” away

24. The Case For Innovation with Graphs
During past 20 years, society has
become hyperconnected.
We considered how regular people
tend to think and reason, and modeled
Neo4j to match that.
Neo4j allows you to naturally map
together the data that matters to you in
a graph — like a mind map!
…
Graph structure scales to many
problems, and is highly flexible to
change.
…
Unlock the business value of
connections and relationships in data.
…
How can we discover insights
hidden behind the complexity?
…
Traditional technology does not
handle complexity well.
…
Information-wise, gone from a small
town to a metropolis. Complexity has
exploded.
…
Need for better insights driven by
competition and disruption. There is a
shift happening now.
The world’s largest companies rely
on Neo4j. The competitive
advantage is real.
…
In uncertain times, many consider
the risks of change, but what are
the risks of not adapting?
…
Get in the driver’s seat. Be the
bringer of innovation.
…
The Problem The Solution The Future

25. THANK YOU!
(me)-[:ASKS_FOR]->(tweet {say: “Neo4j GraphDay:
what a time to be alive!”})-[:FROM]->(you)