FNA’ers Kimmo Soramäki, Ivana Ruffini, Mikhail Oet, Tuomas Takko and Adam Csabay contributed to the recent Risk Book: Data Science in Economics and Finance for Decision Makers, edited by the European Central Bank’s Per Nymand-Andersen.
The chapter FNA authored, Prudential Stress Testing in Financial Networks, provides a taxonomy of organizational problems facing firms operating in financial markets and offers a comprehensive approach to information system design that addresses stress testing in networks.
“It is important because it gives organisations insight and knowledge about unobservable sources and critical means of controlling systemic risk that is otherwise not known to them.”, FNA’s Mikhail Oet, explains.
“Key sources of unobservable systemic risk include the indirect network effects of the behaviour and choices of people and institutions and the markets they operate in. These can be measured by understanding their interconnectedness from structured and unstructured Data sets.”
The FNA Team’s work sits amongst contributions from over 20 global experts from both the private and public sectors.
Per’s book, published in March this year, provides an overview of how digital transformation and data science can support decision making and offers a variety of perspectives on managing digital data.
The book can be purchased here: https://riskbooks.com/data-science-in-economics-and-finance
Following the book release, FNA’s Suptech Lead Adam Csabay caught up with Per Nymand-Andersen for the latest episode in the FNA Talks Series. Per and Adam reviewed the book’s key themes, highlighted its practical contributions and discussed the role that Data Science will play in the post-Covid world.
00:00 – Introduction
01:08 – Why does data science matter for decision-makers in 2021?
05:01 – How can data science help us tackle the impacts and implications of disruptions like the Covid-19 crisis?
08:31 – In what ways is data science transforming the work of central banks?
11:57 – To what extent is a cross-sectoral collaboration (between e.g. technology companies and central banks) needed to explore the opportunities provided by data science?