Applying Network Analytics in KYC

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Applying Network Analytics in KYC
Erik Bijl and Salomon Tetelepta
Data scientists @ Rabobank

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2
Who we are
36 countries
10 million customers
43 thousand employees
Rabobank

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3
Erik Bijl
Data Scientist
Martijn Vlak
Data Scientist
Ciaran Martin
Data Scientist
Our Team
Salomon Tetelepta
Data Scientist
Milou Graas
Analytics Delivery
Manager

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• Integrity of financial system
• Legal requirements
• Minimize digital fraud
Why do we monitor our customers?
> Important to Know Your Customers (KYC)
Criminals launder 13 billion euro
a year in the Netherlands

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© 2022 Neo4j, Inc. All rights reserved.
5
Network analytics has the potential to
significantly improve the effectiveness
of AML programs”
“
McKinsey, 2019
Why use Network Analytics?

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© 2022 Neo4j, Inc. All rights reserved.
6
Finding relationships in data using graph
algorithms and techniques is now
possible at scale.”
“
Why use Network Analytics?
Gartner, 2020

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© 2022 Neo4j, Inc. All rights reserved.
7
Graph Data Science is a science-
driven approach to gain knowledge from
the relationships and structures in data,
typically to power predictions”
“
Why use Network Analytics?
Neo4j, 2020

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© 2022 Neo4j, Inc. All rights reserved.
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Graph Data Science
• Building a graph model
1

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© 2022 Neo4j, Inc. All rights reserved.
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Graph Data Science
• Building a graph model
• Multi-partite graph
• Relations between customers
• Transactions between accounts
1
250 million nodes
1 billion relations

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© 2022 Neo4j, Inc. All rights reserved.
10
Graph Data Science
• Building a graph model
• Feature Engineering
We are here
1
2

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How do we enhance current monitoring?
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Risk
All Customers
Start with known risk
Feature engineering

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Risk
Connected to Risk
All Customers
How do we enhance current monitoring?
Create network features
Feature engineering

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© 2022 Neo4j, Inc. All rights reserved.
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Risk Triangles
Reach: ~10.000 Customers
1. Run cypher query
Feature engineering

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© 2022 Neo4j, Inc. All rights reserved.
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Risk Triangles
Reach: ~10.000 Customers
Feature engineering
2. Find communities (Label propagation)

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© 2022 Neo4j, Inc. All rights reserved.
15
Risk Triangles
Feature engineering
Example case:
• Natural Person
• 76 triangles (44.4%)
• 19/179 relations with risk-customers (10.6%)
AML Experts: Potentially Risky
3. Rank cases on network features

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© 2022 Neo4j, Inc. All rights reserved.
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Conclusions
Promising results
• Current systems can be enhanced
• Ethical and privacy assessment
• Follow-up project started

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Thank you!
Contact us at
Erik.Bijl@rabobank.nl
Salomon.tetelepta@rabobank.nl