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