NODES 2020

1. SAS founded in 2013 in Paris | https://linkurio.us | @linkurious
Stopping money
laundering with graph
analytics.

2. Today’s agenda.
1. Primer on anti-money
laundering
2. AML and graph analytics, a
match made in heaven
3. Concrete examples

3. What is anti-money
laundering (AML)?
1. Collection of
dirty money
2. Placement
3. Layering
4. Integration

4. The FinCEN files
show how difficult
stopping financial
crime is.

5. It’s hard to connect
the dots.

6. The graph approach
provides a
foundation to build
a 360° view of the
clients.

7. Why use graph
technology in AML?
Traditional
approach
Graph
approach
Slow investigations Investigations up to 5x faster
Uncertain decisions Act with a complete picture
Analytics geared towards individual
behaviors
Focus on the complex schemes of
criminal networks

8. Linkurious
Enterprise, the
investigation
platform for graph
data.

9. Our synthetic AML
dataset.

10. A wide range of AML
use cases can be
tackled with graph
analytics.
Structuring. Synthetic
identities.
UBOs.
AML
investigations.
Sanctions
screening.
Entity
resolution.

11. Local exploration:
who this person
sent money to in
the last 30 days?
MATCH (a:BANK_ACCOUNT)
WHERE ID(a) = 10272
WITH a
MATCH p = (a)-
[:HAS_TRANSFERED|HAS_BANK_ACCOUNT*..3]-
(b:PERSON)
WITH p
WHERE ALL (rel IN relationships(p) WHERE
datetime(rel.date) < datetime() - duration({days:
30}) OR rel.date IS NULL)
RETURN p

12. Local exploration:
who are the
Ultimate Beneficial
Owners (UBOs) of
this company?
MATCH (a)
WHERE ID(a) = 15
WITH a
MATCH p = (a)<-[:IS_SHAREHOLDER_OF*..10]-(b)
WHERE ALL (
r IN relationships(p)
WHERE r.ownership > 0.20
)
RETURN p

13. Global exploration:
who received
>$10K in small cash
deposits
(structuring)?
MATCH (a:BANK_ACCOUNT)-[r:HAS_RECEIVED]-
>(b:TRANSACTION)
WHERE datetime(b.date) > datetime() -
duration({days: 90}) AND b.type = "Check"
WITH a, collect(r) as rels, sum(b.amount) as total
WHERE total>10000
RETURN a,rels,total

14. Global exploration:
are there money
mules (structuring)?
MATCH p = (a:TRANSACTION)-[:HOP*3..10]-
>(b:TRANSACTION)
WHERE b.amount > 0.7 * a.amount AND a.amount
> b.amount AND localtime() +
duration.between(date(a.date),date(b.date)) <
localtime() + duration({days: 90})
WITH p, nodes(p) as tx
WHERE ALL(idx in range(0, size(tx)-2) WHERE
(tx[idx]).date < (tx[idx+1]).date)
RETURN p

15. Global exploration:
are there people
sharing personal
information (fake
identity)?
CALL gds.graph.create.cypher(
'individual-graph',
'MATCH (n) WHERE n:PERSON RETURN id(n) AS
id',
'MATCH (a)-
[:HAS_IP_ADDRESS|HAS_EMAIL|HAS_ADDRESS|HA
S_PHONE_NUMBER]->(PI)<-
[:HAS_IP_ADDRESS|HAS_EMAIL|HAS_EMAIL|HAS_
PHONE_NUMBER]-(b) RETURN id(a) AS source,
id(b) AS target'
);
CALL gds.wcc.write('individual-graph', {
writeProperty: 'componentId' })
YIELD nodePropertiesWritten, componentCount;

16. How it works.
Data
preparation.
Graph
database.
Linkurious
Enterprise.
Stop criminal
networks.
Your team identifies
the relevant data
sources for your
project.
A graph expert
imports your data in
a graph database, it’s
a matter of days for
structured data.
Your analysts work
with a graph expert
to customize the look
and feel of Linkurious
Enterprise and setup
alerts and query
templates based on
your requirements.
Your analysts
connect to Linkurious
Enterprise or access
it via an existing app
to detect and
investigate criminal
networks.

17. Linkurious
Enterprise and
Neo4j help stop
money
laundering.
Detect new AML schemes
Go beyond the limitations of traditional
analytics to identify UBOs, structuring,
synthetic identities and more.
Accelerate investigations
With a 360° view of your clients, quickly
explore data to build a comprehensive
understanding of any situation and act
confidently.

18. Questions?
www.linkurio.us
contact@linkurio.us