Slides from my plenary talk and interactive demo at SWIFT Operations Forum Europe (SOFE) on 28 November 2013 in Amsterdam.

1. Amsterdam, 28 November 2013
SWIFT Operations Forum
Network Simulations for Business Continuity
Dr. Kimmo Soramäki
Founder and CEO
Financial Network Analytics
www.fna.fi

2. Network Simulation – Interactive Demo
Failure Scenario
Black node = can receive
but cannot send
Normal Scenario
Green node = Liquidity
available
Red node = No, liquidity.
Queues build up.
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3. Real Networks: Fedwire Payment Network ‘Furball’
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4. Fedwire Core
Fedwire Interbank
Payment Network
Fall 2001
Around 8000 banks, 66
banks comprise 75% of
value,25 banks completely
connected
Soramäki, Bech, Beyeler, Glass and Arnold (2007),
Physica A, Vol. 379, pp 317-333.
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5. SWIFT Message Flows
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6. International Remittances
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7. More Network Maps
Federal funds
Bech, M.L. and Atalay, E. (2008), “The Topology of
the Federal Funds Market”. ECB Working Paper No. 986.
Italian money market
Iori G, G de Masi, O Precup, G Gabbi and G
Caldarelli (2008): “A network analysis of the Italian
overnight money market”, Journal of Economic
Dynamics and Control, vol. 32(1), pages 259-278
Unsecured Sterling
money market
Wetherilt, A. P. Zimmerman, and K. Soramäki
(2008), “The sterling unsecured loan market
during 2006–2008: insights from network
topology“, in Leinonen (ed), BoF Scientific
monographs, E 42
Cross-border bank lending
Minoiu, Camelia and Reyes, Javier A. (2010). A network analysis of global
banking:1978-2009. IMF Working Paper WP/11/74.
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8. Network Theory
Financial
Network Analysis
Social Network
Analysis
Network Science
NETWORK
THEORY
Graph & Matrix
Theory
Computer
Science
Biological
Network Analysis

9. Structure of links between nodes matters
The performance of a node
(bank) cannot be analyzed on
the basis its own properties
and behavior alone
To understand the
performance of one node
(bank), one must analyze the
behavior of nodes that may
be several links apart in the
network.
Each affect each.
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10. Networks Brings us Beyond the Data Cube
For example:
Entities:
100 banks
Variables:
Liquidity, Opening Balance, …
Time:
Daily data
Links:
Bilateral payment flows
Links are the 4th dimension to data
Information on the links
allows us to develop better
models for banks' liquidity
situation in times of stress
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11. Modeling the Flows
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12. Predictive Modeling
• Predictive modeling is the process by which a
model is created to try to best predict the
probability of an outcome
• “What is the impact if a large bank has an
operational disruption at noon?”
– Who is affected first?
– Who is affected most?
– What is the impact on my bank in an hour?
• Valuable information for decision making
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13. Short History of Payment System Simulations
•
1997 : Bank of Finland
– Evaluate liquidity needs of banks when Finland’s RTGS system was joined with TARGET
– See Koponen-Soramaki (1998) “Liquidity needs in a modern interbank payment systems:
•
2000 : Bank of Japan and FRBNY
– Test features for BoJ-Net/Fedwire
•
2001 - : CLS approval process and ongoing oversight
– Test CLS risk management
– Evaluate settlement’ members capacity for pay-ins
– Understand how the system works
•
Since: Bank of Canada, Banque de France, Nederlandsche Bank, Norges Bank,
TARGET2, and many others
•
2010 - : Bank of England new CHAPS
– Evaluate alternative liquidity saving mechanisms
– Use as platform for discussions with banks
– Denby-McLafferty (2012) “Liquidity Saving in CHAPS: A Simulation Study”

14. Stress Testing
Basel Committee for Banking Supervision published in
April 2013 document “Monitoring Tools for Intraday
Liquidity Management”. It outlines stress scenarios,
one of which is:
“Counterparty stress:
a major counterparty
suffers an intraday stress
event which prevents it
from making payments “
Stress Simulation Demo
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15. Thank you
Blog, library and demos are available at www.fna.fi
Dr. Kimmo Soramäki
kimmo@fna.fi
Twitter: soramaki
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