Introduction

FNA’s Academic Collaboration is a program to provide tools, data and resources to universities for collaboratively solving challenges faced by policy makers and financial institutions in today’s challenging environment, and for training the next generation of leaders.

Who is it for?

The program is aimed at academics involved in research and/or teaching.

What are the benefits?

Participants in FNA’s Academic Collaboration Program will enjoy the following benefits:

  • Renewable 12-month access to FNA’s award-winning platform at g20monitor.com (for both academics and students) that can be used as part of fintech, finance and network science courses
  • Access to an extensive collection of proprietary and exclusive student projects and research data
  • Work with FNA to deliver engaging courses on network analytics and other relevant topics
  • FNA to feature select research contributions via the FNA website, newsletters and events
  • Recommend best-in-class students for FNA’s Internship Program
  • Access to FNA Seminar Series, which includes recorded and live presentations by leading academics and industry experts including FNA’s Founder and CEO Kimmo Soramaki
  • Hard copy of Financial Risk and Network Theory book – (forthcoming 2nd edition)
  • Support for academic grants

Current Academic Partners

How to apply?

If you are interested in participating in FNA’s Academic Collaboration Program, please contact Adam Csabay at adam@fna.fi.

News and Events

FNA Talks Data Science in Economics and Finance with the Bank of England
FNA Talks Data Science in Economics and Finance with the Bank of England    Adrian Waddy, Data Consultant at Australian Prudential Regulation Authority and Developer at the Bank of England, joins host Adam Csabay to discuss his contribution to the Risk books publication, Data Science in Economics and Finance for Decision-makers. Adrian’s chapter, Implementing Big […]
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Reconstructing and Stress Testing Credit Networks
By Amanah Ramadiah, Fabio Caccioli, Daniel Fricke Financial networks are an essential source of systemic risk. Unfortunately, detailed data on (direct and indirect) interactions between individual financial institutions is often unavailable, and the only the total aggregate position is known. To conduct a stress test, one must resort to network reconstruction methods to infer the […]
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