How to use graphs and Neo4j to detect tax fraud? A concrete example.

1. VAT fraud : the
mysterious case of
the missing
trader.
SAS founded in 2013 in Paris | http://linkurio.us | @linkurious

2. Introduction.
Jean
Villedieu
Co-Founder of
Linkurious
A mix of fraud and
graph expertise.
>5 years in consulting
MSc Political sciences
and Competitive
Intelligence
Scott
Mongeau
Data Scientist @
SARK7
Fraud Expert
PhD in Business
Analytics Mgmt and
MBA

3. Father Of
Father Of
Siblings
What is a graph ?
This is a graph.

4. Father Of
Father Of
Siblings
This is a node
This is a
relationship
WWhhaatt iiss aa ggrraapphh :? n /o Ndoedse asn &d rreellaattiioonnsshhiippss.
A graph is a set of nodes
linked by relationships.

5. Differents domains where graphs are important.
Some of the domains in which
our customers use graphs.
Supply chains Social networks Communications
People, objects, movies,
restaurants, music…
Suggest new contacts, help
discover new music
Antennas, servers, phones,
people…
Diminish network outages
Supplier, roads, warehouses,
products…
Diminish transportation cost,
optimize delivery

6. A very profitable business.
£176 million
In 2012 in the UK, a fraud ringleader was found guilty of
defrauding £176m in a VAT scam.
Source : http://www.theguardian.com/uk/2012/jul/08/carousel-tax-fraud-mobile-phones

7. How does the VAT fraud works.
Company B sells the
phones to company D
(US) and claims a VAT
refund.
The directors of A and D
disappear with €2M in
stolen taxes.
Company B sells the
phones to company C.
It charges €10M + €1M
for the VAT.
Company A (US) sells to
Company B (Europe)
€10M worth of phones.
A €10M B €10M + €1M VAT C
€1M VAT
refund
€10M
Tax
Agency
D
€1M for A and
€1M for B
Step 1 Step 2 Step 3 Step 4

8. The execution of the fraud
can take place in just a few
weeks.
The tax agencies have data
but it exists in silos making it
hard to piece it together.
Why it is so hard to catch the fraud.
The 3 challenges all tax
authorities face.
Apparences Speed Silos
The companies and
transactions used for the
fraud appear legitimate.

9. How to make sense of complex data.
How can graph
technologies helps?

10. Company registry,
transaction history,
financial criminals list, tax
claims...
Different data sources.

11. Graphs help make sense of complex data.
A graph model help see the
connections in the data.
country : Italy
age : 29
criminal_status
: unknown
Paul
(Person)
Nicole
(Person)
Company A
(Company)
Company C
(Company)
Company B
(Company)
country :
USA
type : LLC
creation_date
: 08/10/1983
country : Italy
type : SRL
creation_date
: 04/09/1984
country : Italy
type : SRL
creation_date
: 18/04/1990
SELLS_TO
COLLECTS_VAT
item : phones
date :
05/08/2014
amount : 1M
SELLS_TO
PARENT_OF
country : USA
age : 53
criminal_status
: unknown
DIRECTOR_OF
DIRECTOR_OF
DIRECTOR_OF

12. Can we use the data to
detect fraud cases?
How to use the information.

13. Designing a fraud detection pattern.
A fraud expert designs a fraud
detection pattern.
I know what to look for. Usually my fraud cases
involve :
● a set of at least three transaction that includes
companies from two different countries ;
● the company in the middle has been created
less than 90 days ago ;
● the transactions occur in a less than 15 days ;

14. Designing a fraud detection pattern.
The pattern is translated in a
graph language.
MATCH p=(a:Company)-[rs:SELLS_TO*]->(c:Company)
WHERE a.country <> c.country
WITH p, a, c, rs, nodes(p) AS ns
WITH p, a, c, rs, filter(n IN ns WHERE n.epoch - 1383123473 < (90*60*60*24)) AS
bs
WITH p, a, c, rs, head(bs) AS b
WHERE NOT b IS NULL
WITH p, a, b, c, head(rs) AS r1, last(rs) AS rn
WITH p, a, b, c, r1, rn, rn.epoch - r1.epoch AS d
WHERE d < (15*60*60*24)
RETURN a, b, c, d, r1, rn

15. Graph databases can tackle big datasets.
A graph database handles the
data analysis at scale.
ETL
Traditional
databases.
Graph
database.
The graph databases helps store the data from various sources and analyse it in real-time to
identify potential fraud cases.

16. An analyst examines the potential fraud cases.
A fraud analyst investigates the
potential fraud cases.
I need to make sure the alerts detected by our
detection system are legitimate. If they are, I need to
understand which companies and which individual
are involved.

17. Visualization transforms alerts into actions..
Graph visualization facilitate
the data investigation.
ETL API
Traditional
database.
Graph
database.
Graph
visualization.
Graph visualization solutions like Linkurious help data analysts investigate graph data faster.

18. Visualizing the results of our pattern.
Two suspicious chains of
transactions.
Companies detected by our query : in dark green US companies, in orange Italian Companies
and in light green UK companies.

19. Looking at the full VAT fraud scheme.
The transactions are
connected in a larger scheme.
The people and companies connected to our initial transactions : in pink the companies, in purple
the holdings and in green the people.

20. Zooming in on a potential criminal.
We can focus on key
individuals.
Looking at Cletis Bysshe, the man at the start of the transactions chain.

21. Graphs can improve your
fraud detection system.
Linkurious allows the fraud
teams to go deep in the data
and build cases against fraud
rings.
The fraud teams acts faster
and more fraud cases can be
avoided.
Detect fraud
cases
Graph databases can find
suspicious patterns hidden in
big data.
Accelerate the
investigations
Save
money
Graphs and fraud detection.

22. Try Linkurious.
You can do it too!

23. Contact us to discuss your projects
at contact@linkurio.us
Conclusion

24. Additional resources.
GraphGist : http://gist.neo4j.org/?d882df51a4775a6b7588
Blog post on the carousel fraud : http://linkurio.us/vat-fraud-mysterious-case-missing-
trader/
Article on fraud and network analysis : http://sctr7.com/2014/08/18/571/
Sample dataset : https://www.dropbox.com/s/t63hzqt2omh9c36/VAT%20fraud%
20detection%20neo4j.zip?dl=0