This document discusses how graph databases can be useful for fraud detection. It begins with an introduction to graphs and graph theory, then discusses how graph databases work and their advantages over relational databases for complex querying and modeling connected data. The document notes that fraud detection relies on real-time analysis, complex patterns, and graph algorithms to navigate relationships. It provides a short demonstration and discusses case studies where graph databases have been successfully used for fraud detection due to their ability to efficiently handle large, interconnected datasets.

1. Graphs
for
Fraud
Detec1on
rik@neotechnology.com
@rvanbruggen

2. Agenda
• About
Graphs
• About
Graph
Databases
• Why
Graph
Databases
ma<er
for
Fraud
Detec?on
– Short
demonstra?on
• Case
Studies
• Q&A

3. Introduc?on:
about
Graphs

5. Meet
Leonhard Euler
• Swiss
mathema?cian
• Inventor
of
Graph
Theory
(1736)

6. Königsberg
(Prussia)
-­‐
1736

7. A
B
D
C

8. A
B
D
C
1
2
3
4
7
6
5

9. About
Graph
Databases

10. So
what
is
a
graph
database?
• OLTP
database
– “end-­‐user”
transac?ons
• Model,
store,
manage
data
as
a
graph

11. What
is
a
graph?
Node
Rela?onship

12. Contrast
with
Rela?onal
Graphs are often referred to as “Whiteboard Friendly”. The
data model reflects the way a domain expert would naturally
draw their data on a whiteboard
“The schema is the data”. Schema flexibility allows the system
to change in response to a changing environment

13. What
are
graphs
good
for?
Complex
Querying

14. Examples
of
complex
queries?
1.
Semi-­‐structure
in
datasets
1
– Normaliza?on
introduces
complexity
– Forces
developers
to
develop
all
kinds
of
logic
to
deal
with
this
variability
in
their
applica?on
logic

15. Examples
of
complex
queries:
2.
Connectedness
in
data
Lots
of
normalized
rela?onships
between
the
different
en??es,
forces
developers
to
do
• Deep
joins
• Recursive
joins
• Pathfinding
opera?ons
• “open-­‐ended”
queries

16. Examples
of
Connectedness

17. Graphs
in
Fraud
Detec1on?

18. Recommender
Fraud
Detec?on
systems

19. Recommender
Fraud
Detec?on
systems
Fraud Detection

20. Graphs
in
Fraud
Detec?on
Systems
• Real
?me
aspect
• Detec?on
Prac?ces
rely
on
Graph
Algorithms
• Opera?onal
efficiency

21. Real
1me
fraud
detec?on?
• Context
is
everything
– You
don’t
want
to
be
blocking
credit
cards
for
no
reason…
false
posi?ves
are
fatal…
• Complexity
maers
– Need
to
outsmart
the
“bad
guys”
– Assume
that
they
can
and
will
understand/beat
the
system
• Visualiza?on
maers
– Manual
interven?on
relies
on
fast
understanding
of
context
–
and
visualiza?on
helps
there

22. Fraud
Detec?on
prac?ces:
Graph
Algorithms
● Helpful
for
naviga?ng
complex
networks
● tell
me
how
A
and
B
are
related
● The
things
on
the
path
between
A
and
B
could
very
well
be
interes?ng
● ShortestPath,
AllShortestPaths,
Weighted
ShortestPath
(Dijkstra,
A*)
● Helpful
for
understanding
the
important
parts
of
a
network
● Clusters
● Bridges
● Centrality
● Betweenness
Centrality
● (Page)Ranking

23. Opera1onal
Efficiency
• Graph
datamodel
removes
the
need
for
many
“batch
opera?ons”
– No
need
to
precalculate
–
just
feed
it
into
the
graph
• Complex
paern
matching
in
milliseconds
• Graph
Locality
==
Predictability
Speed,
even
over
large
datasets

24. Short
demo
-­‐
1
● Circular
paerns
omen
indicate
some
kind
of
aempt
to
“trick
the
system”

25. Short
demo
-­‐
2

26. Use
Cases
(neo4j.com/use-­‐cases)

27. Customers
(neo4j.com/customers)

28. Graph
Gists
(hp://gist.neo4j.org/)

29. Neo4j
versions
/
licenses
Neo4j License Overview
Developer!
Seats!
Personal
Startup
/
Departmental
Enterprise
deployment
models
($6K*/Developer/Year)
Test!
Instances!
($6K/Instance/Year)
Production!
Instances!
(Bundle / Core Pricing)
Open
source
Commercial
license
terms
available
Specific
OEM
models
Instances whose purpose is to
ensure that the software accessing
Neo4j is meeting specification.!
!
(e.g. System Test, Integration Test,
UAT, Performance Test, Staging)
Instances that store and process
data in a way that benefits and
advances an organization’s goals.!
!
May be accessed by applications
and/or end users
Includes access by programmers
to licensed test instances, and
private instances on the
programmer’s personal machine
for the sole purpose of writing,
debugging, or testing software
designed to access Neo4j
*Or otherwise, depending on the Bundle, and negotiation

30. Future
trainings
events!
3

31. QA,
Conclusion,
Next
Steps
Neo
Technology
www.neotechnology.com
Neo4j
www.neo4j.org
rik@neotechnology.com
or
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