This document summarizes Kimmo Soramäki's presentation on applications of network theory in finance and production. The presentation discusses: 1) Using network analysis of the Fedwire payment system to identify influential banks after the 2008 financial crisis. 2) The launch of a new journal on network theory in finance with an editorial board including experts from the Bank of England and ECB. 3) Upcoming conferences in September 2015 on network theory applications in finance.

1. APPLICATIONS OF NETWORK THEORY
IN FINANCE AND PRODUCTION
World Bank
Washington DC
15 May 2015
Kimmo Soramäki
Founder and CEO
FNA Ltd.
FNA

2. First Financial Networks
Fedwire Interbank Payment Network (Fall
2001) was one of the first network views into
any financial system.
Of a total of around 8000 banks, the 66 banks
shown comprise 75% of total value. Of these,
25 banks completely connected
The chart was subsequently used e.g. in
congressional hearings to showcase the type
of information that should be collected by
financial institutions after the financial crisis.
The research is cited in ~300 academic
publications.
Research
paper:
Soramaki,
K.
M
Bech,
J.
Arnold,
R.J.
Glass
and
W.E.
Beyeler,
The
Topology
of
Interbank
Payment
Flows,
Physica
A,
Vol.
379,
pp
317-­‐333,
2007.

3. Call for Papers
New Journal ‘Network Theory in Finance’ is
launched in March 31, 2015
Editor in Chief: Kimmo Soramäki
Editorial Board included e.g.
Andrew Haldane, Bank of England
Franklin Allen, Imperial College/Wharton
Ignazio Angeloni, ECB
“Journal of Network Theory in Finance is an
interdisciplinary journal publishing rigorous and
practitioner-focused research on the application of
network theory in finance. The journal connects
academia, regulators and practitioners in solving
important issues around financial risk”
2nd Annual Conference on
9 September 2015 in Cambridge, UK.
Call for papers is out!
Submit paper to kimmo@fna.fi

4. Global Network of Payment Flows
Agenda
Industry Level Value Chains
Correlation Networks

5. 1. Global Network of Payment Flows
Gottfried Leibrand, CEO of Swift, presenting
the research at Sibos ’14 in Boston

6. Network Maps
Big data problem: Three billion messages exchanged among banks in
231 countries. We focus on aggregated links among countries.
Analysis and visualization a challenge. We don’t want to show much
information (as below).

7. Evolution of Links
Of the 1054 links
gained until 2007, in
74% one (or both) were
rated as medium or low
on the United Nations
Human Development
Index.
Of the 990 links lost
after 2007, 80%
involved at least one
country listed as an
offshore financial center.

8. Total Messages
The number of messages is 5.5% lower (post-crisis than they would have been
had the pre-crisis trend continued unabated throughout the entire period.
The cost of financial crisis $5tr?

9. Communities
Are there meaningful subgroups among
the countries?
Can we group the countries so that
messages are sent mostly
within groups?
Modularity - measure of concentration of
links within communities vs.
between communities.

10. Communities

11. Communities and their hubs

12. Swift vs Remittances
Swift Remittances
2012

13. Most important and most volatile countries
Most important
Most volatile

14. 2. Industry level value chains

15. IO data + enhancements
Top line: sales by industry
industry sales to industry (IDI)
industry sales to wholesalers (ITW)
industry sales to retailers (ITR)
industry sales to final demand (IDFD)
Second row: sales by wholesale trade,
mainly sales to industry (WTI)
wholesale to wholesale (WTW)
wholesale to retailers (WTR)
wholesale to final demand (WTFD)
Third row: retailers’ sales to industry (RTI),
retail sales to wholesalers (RTW)
retail sales to other retailers (RTR)
retail sales to final demand (RTFD)
Bottom line: industry value added (IVA)
wholesale distribution margins (WDM)
retail distribution margins (RDM)
US government sourced and enhanced
input output data showing value of trade
between industries. Sourced by FNA
Partner, Arium Ltd.

16. Developing a scenario
Example: Bisphenol A (BPA) scenario
BPA is an endocrine disruptor in some species. Possible harm to humans
includes heart disease, diabetes, obesity, breast cancer, male infertility and
general behavioural problems.
Process:
1. Develop value chains. Which
industries are affected down
the chain?
2. Overlay portfolio. Which policies
are in the affected lines of business
in the affected industries?
3. Calculate loss captured in
scenario based e.g. on
assumptions on the severity of the
event.

17. 3. Visualizing Correlations
…"
Example: Daily returns of asset prices
(ETFs)
Difficult to understand large-scale
correlation or other dependence structures
of time series data (such as asset prices).
Objective is to:
Efficiently represent a complex system
moving in time
Visualize and predicts stress events in
their context
Overlay multiple dimensions of the data to
allow for visual inference of information

18. Examples
Collapse of Lehman Brothers 15 Sept 2008 EU Debt Crisis 2009 -
Energy Meltdown 2014/2015
Other maps:
GSIFIs, FX, Sovereign debt,
HPIs, Interest Rates, Stress
Indicator (CFSI), Equity indices,
Commodities, Balance sheet
items, trading volumes, etc.

19. FNA HeavyTails

20. Collapse of Lehman Brothers on 15 Sept 2008
View Interactive Dashboard

21. Greek Debt Crisis 2009 -
View Interactive Dashboard

22. Gold Crash of April 2013
View Video

23. Energy Meltdown 2014/2015
View Video

24. Summing up
Many risk models can be improved by taking into account
links in the data: interconnections, covariances,
dependencies, flows, exposures, co-occurances, etc …
Major challenges that we face are related to filtering signal
from noise in large networks and presenting the information
efficiently.
Much of the work can be summed up as:
Creating a Map - Placing you on Map - Providing Directions

25. The FNA Software consists of FNA Platform
and FNA Apps.
FNA Platform is the server side workhorse
for analysis, simulation and visualization of
financial networks used by all FNA Apps.
FNA Software
FNA Apps master particular uses
cases with an interactive user
experience.
FNA Maps
FNA Payments
FNA HeavyTails

26. FNA Platform
Over a decade in making and with a wider selection of
financial network algorithms than any other software, the
FNA Platform offers a comprehensive end-to-end enterprise
solution for advanced analysis and visualizations of financial
networks.
FNA Platform is the backbone of all FNA Apps and available
as a cloud-based solution with a RESTful API, as an enterprise
installation, as a Desktop software and as a Java library.
Cutting-edge analytics
Calculate hundreds of graph metrics, perform
cluster analysis and carry out predictive stress
tests and simulations.
Complete documentation
with over 500 pages of manuals describing the
platform’s functionality with examples, tutorials
and real-life applications.
End-to-end automation
Develop scripts for fully automated and regular
analytics or use FNA REST API from external
applications.
Easy integration
tap to data most common online data sources
and vendors directly, or from local databases.
More at www.fna.fi/platform

27. FNA HeavyTails
FNA HeavyTails helps risk managers and portfolio managers
identify and communicate emerging risks and design adaptive
stress tests.
FNA combines advanced network theory and interactive data
visualizations to detect hidden patterns in complex data.
FNA HeavyTails implements cutting edge research in Financial
CartographyTM by FNA and its collaborations with top
universities. The HeavyTails dashboard makes these analytics
readily accessible through a beautiful user interface.
Monitor systemic risk
with FNA’s unique correlation maps, Value-at-Risk
(VaR) analytics and outlier detection.
Stress test portfolios
with FNA’s interactive ‘Rapid stress testing’
functionality and integrate them with your
portfolio management and risk systems.
Identify emerging risks
with statistical and visual detection of outlier
assets, days, and periods.
Evaluate investment strategies
with correlation and clustering analysis against
benchmarks, and quickly identify hidden
concentration risk.
More at www.fna.fi/heavytails

28. FNA Maps
FNA NetworkMaps helps financial institutions explore complex
financial data for managing risks, identifying new opportunities
and making better, data driven decisions.
Combining advanced network theory with interactive
visualizations FNA NetworkMaps gives its users the analytical
power to answer the most difficult questions that they face.
Find hidden patters
with the help of hundreds of graph metrics,
clustering analysis and and predictive stress tests
and simulations.
Connect the dots
with fast interactive data exploration powered by
algorithms that filter signal from noise and FNA’s
beautiful network maps.
Monitor
the network in real-time and get alerted by
abnormal events.
Communicate
Create interactive network maps from public or
internal data sources and share them freely
online or within your organization.
More at www.fna.fi/maps

29. FNA Payments
FNA PaymentSimulator helps financial market infrastructures
and central banks model liquidity and operational risks,
evaluate alternative system designs and carry out stress tests.
FNA PaymentSimulator methodologies are based on leading
research in network theory and financial infrastructures by FNA
and its collaborations with top universities and central banks.
The interactive dashboard makes these advanced analytics
easily accessible through a well-thought user interface.
Monitor system participants
with comprehensive network maps and risk
metrics, including FNA’s SinkRank™ and metrics
proposed by BIS/BCBS.
Identify emerging risks
with statistical and visual detection of outliers
activity.
Get the big picture
and drill into details. Uncover interlinkages and
second-order effects with FNA’s unique network
maps.
Carry out predictive stress tests
and payment simulations and explore the results
visually or numerically.
More at www.fna.fi/payments

30. Dr. Kimmo Soramäki
Founder and CEO
kimmo@fna.fi
FNA
Dr. Eugene Neduv
VP Solutions
eugene@fna.fi