This document summarizes Cerved Group's use of Neo4j and graph databases. Cerved processes large amounts of data on companies and individuals to provide credit risk management, marketing, and other services. Neo4j allows Cerved to more efficiently analyze relationships between entities, such as beneficial owners of companies. Cerved's Graph4You platform makes some of this graph data accessible to customers and data scientists to explore use cases. Cerved sees graph databases and extracting additional insights from relationships in data as important to its future.

1. Rome, 8th March 2017
Fueling innovation in finance
The Italian business graph
Stefano Gatti – Head of Innovation & Data Sources

2. 2
Index
Overview
Cerved Group
Cerved & Neo4J
Exploring data
Neo4j for data scientists
Toward the future
Free thinking
Graph4You
Neo4j for innovation
The starting point …

3. Overview

4. 4
Our business & figures
CREDIT INFORMATION
MARKETING SOLUTIONS
CREDIT MANAGEMENT
1000 report/min
✓ Documents
50 million
✓ Lines of code
34,000
✓ Customers
59 million
✓ Payments data
1,800
✓ People
377 million Eur (2016)
✓ Revenue
Protect your business against credit risk
Find new business opportunities
Optimize credit management processes

5. 5
Our “big data”
Web Data
Open Data
Proprietary data
Official data
Chamber of
Commerce
official data
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6. Cerved & Neo4J

7. 7
The beneficial owner of a company
Level 1: 10% Level 3: 24.4% Level 4: 34.2%
ACME spa
Soc. A (40%)
Soc. B (50%)
Willy (10%)
Soc. A1 (40%)
Soc. A2 (60%)
Soc. B1 (40%)
Soc. B2 (60%)
Willy
(40%)
Duffy
(60%)
Willy
(40%)
Soc. B1.1 (60%)
Soc. B2.1 (50%)
Speedy
(50%)
Willy
(90%)
Bunny
(10%)
Level 2: 10%

8. 8
2012: a real-time & IT-cost problem … solved!
Relational database Neo4J
Average time 369 ms (till 4th level)
> 12 sec (from 4th level)
39 ms (till 15th level)
• Computed on 2.238.000 companies &
2.756.000 distinct people
• About 150.000 computation/hour
• More than 350.000 push notification to
our customers (mainly banks) in last 6
months
Neo4J in cluster configuration
(two standard servers 12GB
RAM) with usage of Traversal
Framework & In-Memory
Caching. Average usage of CPU
< 5%
Level Beneficial Owners
1 3.379.755
2 202.194
3 44.589
4 11.236
5 3.403
6 1.364
7 464
8 304
9 123
10 61
11 36
12 16
13 6
14 4
15 2
2016 - Statistics

9. 9
Aracne Administration Dashboard
5 kinds of relationships :
• Actual Shareholders
• Past Shareholders
• Actual Managers
• Past Managers
• M&A Operations
2 kinds of nodes :
• Companies
• People (Managers & Shareholders)

10. Graph4You

11. 11
2015 - Graph4you: from datascientists to the market
Born as a living datascientist lab for exploring data has easily matched some use-cases of our customers
graph4you.cerved.com
http://developer.cerved.com/
Nodes
• Companies
• Registered 8,1 M
(of which) not active 1,9 M
• Not Registered 1,6 M
• Not Italian 320 K
• People 16,4 M
• Locations 4,5 M
• Customer data
Relationships
• Manager 15,6 M
• Shareholders 8 M
• Building property 14,7 M
• Current address 16,7 M
• Beneficial Ownership 3,6 M
• M&A 100K
Filter:
• Kind of nodes
• Connection degree
• Kind of relationship
• Property of relationship
=͂ 30 M
Nodes
=͂ 59 M
Egdes

12. 12
Graph4you
Architecture diagram
WEB APP
FRONT - END
SPRING BOOT
LINKURIOUS.JS
BOOTSTRAP
CUSTOM CODE
JASPER REPORT
SPRING BOOT
SPRING
JAVA
Shortest Path
Traversal
REST API
BACK - END
API
Transactional
HTTP API
INGESTION
Graph fileMySQL

13. Graph4YouLive

14. 15
Graph4You - API
User AppUX
Developer
API Dev Portal
API Graph4You
Cerved
“big data”
Consumer Solution
Cerved Smart
Data API
Framework

15. 16
Graph4You - API

16. Neo4j for data scientists

17. 18
Cerved corporate linkages
Made using Neo4J as OLTP database and computing in-memory the proprietary algorithm
Made in 2014 in partnership with SPAZIODATI
22%
Better recall
30%
More precision
with more data &
managing cycles
Near
Real Time
Levels of depth Frequency
1 178.542
2 36.435
3 5.044
4 913
5 137
6 44
7 13
8 1
9 2
Tot 221.131
Analytics on Neo4J

18. 19
Italian industry 4.0 case study
Mixing many kinds of data in a graph …

19. Work for future

20. 21
Cerved future in graph journey …
Critical Chain (Ex: Contagion Risk)
next
Corporate linkages in Graph4You
Work in Progress
Company similarity
Work in Progress

21. 22
Cerved big data journey: «lessons learned»
Augmented data
Extracting value from existing
data with technology
Extracting new data with
technology

22. 23
Future is not what is used to be …
"The illiterate of the 21st century will not be those
who cannot read and write, but those who cannot
learn, unlearn, and relearn."
Alvin Toffler

23. Stefano Gatti
mailto: stefano.gatti@cerved.com
Twitter: @micio1970
Thanks!
Not Cerved images in the presentation © Jurgen Appelo, Creative Commons 3.0 BY http://www.management30.com/