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An overview into implementation of Graph in battling Financial Economic Crime
Graph Technology at Rabobank
Stijn Oude Elferink
Product Manager Network Analytics
Rabobank at a Glance
2
International
Private sector lending to
Food & Agri
€ 72.6 billion
Private sector lending to Trade,
Industry and Services
€ 40.2 billion
Leasing
€ 40.5 billion
The Netherlands
Financial Economic Crime is a hot topic in
banking
3
Our journey in Network Analytics started in the Financial
Economic Crime domain
4
Network analyses of each individual customer can be
used as input to boost the accuracy of transaction
monitoring models”
Central Bank of the Netherlands, 2022
“
Network analytics has the potential to
significantly improvethe effectiveness
of AML programs”
McKinsey, 2019
“
To effectively adopt Network Analytics the people,
processes and technology need to change
5
People
Technology
Process
Network
analytics
All individuals involved in the transition. This
includes the employees that execute tasks,
managers who set vision and make decisions, or
project stakeholders with their own
organizational goals.
The tools and systems used to support or enable
the organization to carry out processes more
efficiently
The actions enabling the foundation that aligns
people with the culture and quality of work a
project or initiative needs.
Source: The People, Process, Technology (PPT) Framework - Whatfix
People: Make graph technology
comprehensible
Creating a story to explain the concept of Network
Analytics for non-data-savvy FEC stakeholders
Utilizing the power of a network: visualization
Building an expert team of data scientist and engineers
to get started!
6
Process
Experimenting gives the
freedom to fail
7
“
Process: Mature the graph expertise
8
Example: Risk identification in Correspondent Banking
9
• What is the use case?
Correspondent Banking are services provided to foreign banks to conduct business transactions on their behalf.
Although the sender and receiver are not a customer, these transactions need to be monitored.
Solving correspondent banking’s woes - The Banker
Danske Bank to pay $2bn penalty for defrauding US banks (ft.com)
Example: Risk identification in Correspondent Banking
10
• What is the use case?
Correspondent Banking are services provided to foreign banks to conduct business transactions on their behalf.
Although the sender and receiver are not a customer, these transactions need to be monitored.
• What is the hypothesis?
Network Analytics can uncover new risk undetected by existing monitoring systems
A. Janssen Australian
bank
Belgium
bank
Rabobank F. Peters
has account at has account at
transacts to
transacts to
Example: Risk identification in Correspondent Banking
11
• What is the use case?
Correspondent Banking are services provided to foreign banks to conduct business transactions on their behalf.
Although the sender and receiver are not a customer, these transactions need to be monitored.
• What is the hypothesis?
Network Analytics can uncover new risk undetected by existing monitoring systems
A. Janssen Australian
bank
Belgium
bank
Rabobank
F. Peters
Arnold Jansen Indonesian
bank
Perry St 10, Sydney
Perry Street, Sydney
Hoofdstraat 5, Antwerp
ICIJ
has account at has account at
has address
has account at
has address
has address
mentioned in
Process: Features to implement in existing systems
12
Process: Leveraging research to pave the way
13
Reduction of more than 30 % False Positives
Example: A transaction network to detect fraud
14
Fraudster
Victim
• What is the use case?
• External Fraud is detecting fraudulent transactions in real-time
• It is a legal obligation to protect our customers.
• Network analytics can provide new insights which can serve as an
indicator for fraud
• What is the hypothesis?
A victim and fraudster are not part of the same community
In the experiment we have tested several different
Neo4J GDS Algorithms on ‘Community Detection’
and ‘Pathfinding’ to compare which indicator
worked best as a fraud indicator.
15
Storage
The storing of data that can handle highly connected data from different data sources or formats and enable querying of data in a
very efficient and fast way or enables an overlay on top of existing data storage solutions.
Visualization
Presenting a graph structure in a visual way, to support users in
an easy way to execute their dependency analysis and helping
them navigate the network structure for a specific use case.
Processing
Applying analytics through algorithms to support different
approaches on processing graph data structures.
Technology: Building products and services on top of
graph technology
Connect with me via LinkedIn
Q & A

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Rabobank_Exploring the Impact of Graph Technology on Financial Services.pdf

  • 1. An overview into implementation of Graph in battling Financial Economic Crime Graph Technology at Rabobank Stijn Oude Elferink Product Manager Network Analytics
  • 2. Rabobank at a Glance 2 International Private sector lending to Food & Agri € 72.6 billion Private sector lending to Trade, Industry and Services € 40.2 billion Leasing € 40.5 billion The Netherlands
  • 3. Financial Economic Crime is a hot topic in banking 3
  • 4. Our journey in Network Analytics started in the Financial Economic Crime domain 4 Network analyses of each individual customer can be used as input to boost the accuracy of transaction monitoring models” Central Bank of the Netherlands, 2022 “ Network analytics has the potential to significantly improvethe effectiveness of AML programs” McKinsey, 2019 “
  • 5. To effectively adopt Network Analytics the people, processes and technology need to change 5 People Technology Process Network analytics All individuals involved in the transition. This includes the employees that execute tasks, managers who set vision and make decisions, or project stakeholders with their own organizational goals. The tools and systems used to support or enable the organization to carry out processes more efficiently The actions enabling the foundation that aligns people with the culture and quality of work a project or initiative needs. Source: The People, Process, Technology (PPT) Framework - Whatfix
  • 6. People: Make graph technology comprehensible Creating a story to explain the concept of Network Analytics for non-data-savvy FEC stakeholders Utilizing the power of a network: visualization Building an expert team of data scientist and engineers to get started! 6
  • 8. Process: Mature the graph expertise 8
  • 9. Example: Risk identification in Correspondent Banking 9 • What is the use case? Correspondent Banking are services provided to foreign banks to conduct business transactions on their behalf. Although the sender and receiver are not a customer, these transactions need to be monitored. Solving correspondent banking’s woes - The Banker Danske Bank to pay $2bn penalty for defrauding US banks (ft.com)
  • 10. Example: Risk identification in Correspondent Banking 10 • What is the use case? Correspondent Banking are services provided to foreign banks to conduct business transactions on their behalf. Although the sender and receiver are not a customer, these transactions need to be monitored. • What is the hypothesis? Network Analytics can uncover new risk undetected by existing monitoring systems A. Janssen Australian bank Belgium bank Rabobank F. Peters has account at has account at transacts to transacts to
  • 11. Example: Risk identification in Correspondent Banking 11 • What is the use case? Correspondent Banking are services provided to foreign banks to conduct business transactions on their behalf. Although the sender and receiver are not a customer, these transactions need to be monitored. • What is the hypothesis? Network Analytics can uncover new risk undetected by existing monitoring systems A. Janssen Australian bank Belgium bank Rabobank F. Peters Arnold Jansen Indonesian bank Perry St 10, Sydney Perry Street, Sydney Hoofdstraat 5, Antwerp ICIJ has account at has account at has address has account at has address has address mentioned in
  • 12. Process: Features to implement in existing systems 12
  • 13. Process: Leveraging research to pave the way 13 Reduction of more than 30 % False Positives
  • 14. Example: A transaction network to detect fraud 14 Fraudster Victim • What is the use case? • External Fraud is detecting fraudulent transactions in real-time • It is a legal obligation to protect our customers. • Network analytics can provide new insights which can serve as an indicator for fraud • What is the hypothesis? A victim and fraudster are not part of the same community In the experiment we have tested several different Neo4J GDS Algorithms on ‘Community Detection’ and ‘Pathfinding’ to compare which indicator worked best as a fraud indicator.
  • 15. 15 Storage The storing of data that can handle highly connected data from different data sources or formats and enable querying of data in a very efficient and fast way or enables an overlay on top of existing data storage solutions. Visualization Presenting a graph structure in a visual way, to support users in an easy way to execute their dependency analysis and helping them navigate the network structure for a specific use case. Processing Applying analytics through algorithms to support different approaches on processing graph data structures. Technology: Building products and services on top of graph technology
  • 16. Connect with me via LinkedIn Q & A