Applying Network Analytics in KYC

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This document discusses how Rabobank, a Dutch bank, is applying network analytics to enhance its know your customer (KYC) and anti-money laundering (AML) processes. It describes building a graph model with 250 million nodes and 1 billion relations from customer data. Network features like risk triangles and communities are generated and used to identify and rank potentially risky customer cases for AML experts to review. Initial results were promising and a follow-up project was started to further develop ethical network analytics for KYC/AML monitoring.

Technology

© 2022 Neo4j, Inc. All rights reserved.
Applying Network Analytics in KYC
Erik Bijl and Salomon Tetelepta
Data scientists @ Rabobank

© 2022 Neo4j, Inc. All rights reserved.
2
Who we are
36 countries
10 million customers
43 thousand employees
Rabobank

© 2022 Neo4j, Inc. All rights reserved.
3
Erik Bijl
Data Scientist
Martijn Vlak
Data Scientist
Ciaran Martin
Data Scientist
Our Team
Salomon Tetelepta
Data Scientist
Milou Graas
Analytics Delivery
Manager

© 2022 Neo4j, Inc. All rights reserved.
4
• 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

© 2022 Neo4j, Inc. All rights reserved.
© 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?

© 2022 Neo4j, Inc. All rights reserved.
© 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

© 2022 Neo4j, Inc. All rights reserved.
© 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

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
8
Graph Data Science
• Building a graph model
1

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
9
Graph Data Science
• Building a graph model
• Multi-partite graph
• Relations between customers
• Transactions between accounts
1
250 million nodes
1 billion relations

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

© 2022 Neo4j, Inc. All rights reserved.
How do we enhance current monitoring?
11
Risk
All Customers
Start with known risk
Feature engineering

© 2022 Neo4j, Inc. All rights reserved.
12
Risk
Connected to Risk
All Customers
How do we enhance current monitoring?
Create network features
Feature engineering

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
13
Risk Triangles
Reach: ~10.000 Customers
1. Run cypher query
Feature engineering

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
14
Risk Triangles
Reach: ~10.000 Customers
Feature engineering
2. Find communities (Label propagation)

© 2022 Neo4j, Inc. All rights reserved.
© 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

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
16
Conclusions
Promising results
• Current systems can be enhanced
• Ethical and privacy assessment
• Follow-up project started

© 2022 Neo4j, Inc. All rights reserved.
17
Thank you!
Contact us at
Erik.Bijl@rabobank.nl
Salomon.tetelepta@rabobank.nl

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GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024

Neo4j

GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024

Neo4j

GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...

Neo4j

Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.

GraphAware - Transforming policing with graph-based intelligence analysis

Neo4j

Petr Matuska, Sales & Sales Engineering Lead, GraphAware Western Australia Police Force’s adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcement’s ability to leverage growing volumes of valuable data to prevent crime and protect communities.

GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates

Neo4j

KLARNA - Language Models and Knowledge Graphs: A Systems Approach

Neo4j

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