network simulations for business continuity
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SWIFT Operations Forum Network Simulations for Business Continuity
Dr. Kimmo SoramäkiFounder and CEOFinancial Network Analyticswww.fna.fi
Amsterdam, 28 November 2013
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Failure Scenario Normal Scenario
Network Simulation – Interactive Demo
Black node = can receive but cannot send
Green node = Liquidityavailable
Red node = No, liquidity. Queues build up.
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Real Networks: Fedwire Payment Network ‘Furball’
Fedwire Interbank Payment NetworkFall 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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Fedwire Core
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SWIFT Message Flows
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International Remittances
7Minoiu, Camelia and Reyes, Javier A. (2010). A network analysis of global banking:1978-2009. IMF Working Paper WP/11/74.
Federal fundsBech, M.L. and Atalay, E. (2008), “The Topology of the Federal Funds Market”. ECB Working Paper No. 986.
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
Italian 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
Unsecured Sterling money market
Cross-border bank lending
More Network Maps
NETWORK THEORY
Financial Network Analysis
Biological Network Analysis
Graph & Matrix Theory
Social Network Analysis Network Science
Computer Science
Network Theory
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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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Networks Brings us Beyond the Data Cube
Variables
Entiti
es
Time
For example:
Entities: 100 banks
Variables: Liquidity, Opening Balance, …
Time: Daily data
Link
sLinks:Bilateral payment flows
Information on the links allows us to develop better models for banks' liquidity situation in times of stress
Links are the 4th dimension to data
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Modeling the Flows
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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
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”
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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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Thank you
Dr. Kimmo Soramäki kimmo@fna.fiTwitter: soramaki
Blog, library and demos are available at www.fna.fi
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