Agenda

Agenda

Agenda

Building Blocks of Network Analytics

09:0009:30

Registration & Coffee

09:00 - 09:30

09:3011:00

Network Analytics: The State of the Art

09:30 - 11:00

  • Introduction to Network Theory and overview of the training course

  • Applications of Network Theory in Finance and beyond: Risk Exposure Diagnostics, Market Correlation Detection, Payment Analytics, FMI Design and Oversight

  • Presentation and demonstration of FNA's software featuring a real time graph analytics engine and advanced configurable dashboard for visual investigation of complex data

  • Overview of scenario generation exercises through visual detection of anomalies in market dynamics; reverse as well as forward-looking stress testing

11:0011:30

Morning Break

11:00 - 11:30

11:3013:00

Exposure Networks and Stress Testing

11:30 - 13:00

  • Focus on the practical application of network analytics in measuring, mapping and modelling of financial exposures

  • Network analysis of trade repository data and identification of systemically important financial institutions

  • Understanding complex instruments that may hide substantial risks - as highlighted by the agent-based models in the Financial Crisis

  • Hands-on use of interactive dashboards in relation to Case Studies of e.g. CBMX index, Bank for International Settlement's bank's country risk exposure

13:0014:00

Lunch

13:00 - 14:00

14:0015:30

Correlation Maps: A Systemic View of Financial Markets

14:00 - 15:30

  • Presentation of advanced correlation maps visualising interconnectivity of markets

  • Focus on Value at Risk analytics and outlier detection

  • Development of early warning signals through monitoring of interconnected market dynamics and visual

  • Statistical identification of hidden patterns in complex data

  • Hands-on use of dashboards for Case Studies (e.g. US Housing Bubble and Crisis)

15:3016:00

Afternoon Break

15:30 - 16:00

16:0017:00

Stress Testing Correlation Networks

16:00 - 17:00

  • Focus on financial markets as a complex system with numerous measurable interdependencies

  • Production of 'what if' scenarios to predict movements of markets under stress

  • Stress testing correlation structures

  • Hands-on use of interactive dashboards in relation to Case Studies (e.g. Brexit Referendum and US Presidential Election)

17:0017:01

End of Day 1

17:00 - 17:01

Practical Applications: Understanding, Visualising and Managing Risks

09:0009:30

Coffee Reception

09:00 - 09:30

09:3011:00

Using Network Simulations to Design FMIs

09:30 - 11:00

  • Modelling FMIs as complex systems with 'Agent-Based Modelling'

  • Measuring liquidity efficiency

  • Developing new Liquidity-Saving Mechanisms

  • Hands-on use of interactive dashboards in relation to Case Studies (e.g. CLS, LVTS, CHAPS)

11:0011:30

Morning Break

11:00 - 11:30

11:3013:00

Monitoring and Stress Testing FMIs and their Members for Liquidity, Solvency and Systemic Risk

11:30 - 13:00

  • Using payment data to measure liquidity and solvency of financial institutions

  • Real-time monitoring and outlier detection

  • Developing stress scenarios (participant failure, operational issues, etc.)

  • Using Agent-Based Simulations to evaluate different stress scenarios

  • Running simulations

  • Hands-on creation and analysis of transaction networks (e.g. from Trade repository or payments data)

13:0014:00

Lunch

13:00 - 14:00

14:0015:30

Diagnostic Analytics: Detection and Investigation of Financial Crime and Cyber Attacks

14:00 - 15:30

  • Improving fraud detection and AML with network theory

  • "Following the money" and automating the manual investigation of financial crime

  • Identification of DDoS attack patterns through real-time detection of anomalies in cyber networks

  • Cyber attacks and technological and infrastructure interdependencies

  • Hands-on use of interactive dashboards in relation to Case Studies

  • Hands-on creation and analysis of related party networks

15:3016:00

Afternoon Break

15:30 - 16:00

16:0017:00

Course Conclusion: Next Steps for Network Analytics

16:00 - 17:00

  • Where regulators are (and will be) using network theory – and requiring it

  • Review of key takeaways from earlier sessions

  • “Tomorrow’s” themes, trends and opportunities

17:0017:01

End of Course

17:00 - 17:01

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Kimmo Soramäki

Founder and CEO

Financial Network Analytics

Kimmo Soamäki is the founder and CEO of Financial Network Analytics (FNA) and the founding editor-in-chief of The Journal of Network Theory in Finance. He started his career as an economist at the Bank of Finland where he developed in 1997 the first simulation model for interbank payment systems. In 2004 while at the research department of the Federal Reserve Bank of New York, Mr Soramäki was among the first to apply methods from network theory to improve our understanding of financial systems. During the financial crisis of 2007-2008 he advised several central banks, including the Bank of England and European Central Bank, in modelling interconnections and systemic risk. This work led him to found FNA in 2013 provide solutions to monitor the complex financial networks that play a continually larger role in the world around us. Mr Soramäki holds a Doctor of Science in Operations Research and a Master of Science in Economics (Finance), both from Aalto University in Helsinki.

"In 2019, disruptive technological innovation continues to transform financial systems – and the regulatory and supervisory regimes that protect them. Cryptocurrencies are forcing regulators and supervisors to take sides, cyber threats bring new dimensions to systemic risk, and digitalisation is generating revolutionary platforms for financial crime and fraud.

"Against this backdrop, central bankers increasingly recognise they can no longer afford to be fast followers. Indeed, the proactive application of advanced technologies like Machine Learning or DLT also opens up unprecedented regulatory and supervisory possibilities. RegTech and SupTech offer new tools and frameworks to accelerate risk-based reporting, monitor systemic risk as well as supervise financial institutions based on Big Data. Increasingly, central bankers recognise that in order to make the most of these emerging opportunities, significant change is needed: at methodological, technical and institutional levels.

"Through presentations, collaborative work sessions and hands-on exercises, this training course is designed to equip delegates with the tools to meet these and other current challenges facing central bankers, regulators and supervisors with an active interest in Regulatory and Supervisory Technology."