Neo4j GraphTalk Oslo 2019 Aiko Yamashita, DNB

1. Graph Analytics
at DNB
Exploring the frontiers of Advanced
Analytics with the help of Network
Analysis and Neo4J
Aiko Yamashita, PhD.
Data Scientist
Center of Excellence in Advanced Analytics

3. 2018
PROTECT
2020
GROW
2019
OPERATIONALIZE

4. DNB IS ROLLING OUT A WIDE SET OF
TECHNOLOGIES...
FROM DIGITAL BEHAVIOR
COLLECTION…

5. TO OUR CLOUD-BASED (AWS) INSIGHT
PLATFORM
TO…

6. HARNESSING NEO4J
TO UNDERSTAND
OUR CUSTOMERS
AND THEIR
CONNECTIONS

7. Networks are visual and intuitive
Tables need to be traversed
Sender Account Receiver Account 2 Amount
Sindre 38387 Karl Aksel 123456 50 000
DNB 9918181818 Karl Aksel 123456 70 000
Karl Aksel 123456 Åste 34567 10 000
Schibsted 929237333 Åste 34567 85 000
Åste 34567 Elsa 4567 100
Elsa 4567 Gustav 6789 20
Karl Aksel 123456 Gustav 6789 100
Sindre 38387 Gustav 6789 200

8. Personalisering
Fraud
Personalisation

9. Default
Prediction
Churn Prediction

10. Example 1: Fraud
detection
Synthetic identities

11. A ring of N people (N≥2) sharing M elements
of data (such as name, date of birth, phone number,
address, account number, company id, tax id, etc.) can
create up to NM synthetic identities, where each
synthetic identity is linked to
M × (N-1)other nodes, for a total of:
NORMAL QUERIES IN SQL ARE
UNSUSTAINABLE IF THE COMPLEXITY IN
RELATION EXPLODES!

12. A ring of N people (N≥2) sharing M elements
of data (such as name, date of birth, phone number,
address, account number, company id, tax id, etc.) can
create up to NM synthetic identities, where each
synthetic identity is linked to
M × (N-1)other nodes, for a total of:
NORMAL QUERIES IN SQL ARE
UNSUSTAINABLE IF THE COMPLEXITY IN
RELATION EXPLODES!
(NM × M × (N-1) ) / 2 relationships

13. NORMAL QUERIES IN SQL ARE
UNSUSTAINABLE IF THE COMPLEXITY IN
RELATION EXPLODES!
2187 Nodes
19 683 Relations

14. SELECT DISTINCT co_owner.name
FROM person AS capo
JOIN prop AS prop1 ON prop1.person_id = capo.id
JOIN prop AS prop2 ON prop2.company_id =
prop1.company_id
JOIN …
JOIN …
JOIN …
JOIN …
JOIN …
JOIN …
JOIN …
JOIN …
JOIN person AS co_owner
ON prop2.person_id = co_owner.id AND co_owner.id <>
capo.id
WHERE person.name = 'Pablo Escobar';

15. MATCH (n:Person)-[*]-(o) WITH n, count (DISTINCT o)
AS size WHERE size > 2 RETURN n

16. EXAMPLE 2:
DEFAULT
PREDICTION
16

17. 17

18. We predict default probability for
node based on neighbors..
We calculate a edge weight via
tanh or mutual information rate
We use a Normalization factor
We complement this score with
accounting-related variables

19. EXAMPLE 3:
UNDERSTANDING
OUR CUSTOMERS

20. MATCH (p:Company)
WITH p, SIZE((p)-[:BOARD]->())
as cnt
ORDER BY cnt DESC LIMIT 5
MATCH (p)-[:BOARD]->(c)RETURN p,
c, cnt;
WHO ARE THE PEOPLE
WHO SIT IN MOST
STEERING BOARDS?

21. WHO IS A TOP
INFLUENCER IN THE
NETWORK?
Closeness centrality,
PageRank, Centroids