Big Data: Applications in Economic and Statistical Analysis

Big Data: Applications in Economic and Statistical Analysis

Big Data: Applications in Economic and Statistical Analysis

Course Chair: Per Nymand-Andersen, Adviser, European Central Bank

 

Tuesday - 21 May 2019

Big Data in 2019: Opportunities and Challenges for Central Banks and Regulators

Operating in the new landscape: key dynamics and trends in focus

Aurel Schubert, former Director General Statistics, European Central Bank and Vice-Chairman, Irving Fisher Committee

 

  • Key forces and dynamics shaping the work of economists, statisticians and data analysts in central banks
  • Evolution of data needs for monetary and macro-prudential policy making
  • Opportunities and challenges related to new international standards and initiatives
  • Discussion: how do central banks need to change to make the most of innovation?

Big Data and FinTech: digital transformation in finance and economics

Jyry Hokkanen, Head of Statistics, Sveriges Riksbank

 

  • Overview of recent trends and developments in disruptive technological innovation
  • Key statistical and computational technologies behind “the rise of Big Data”
  • The impact of Big Data on financial services industry and the world of economics
  • Implications for information architecture and statistical methodology in central banks

Big Data analytics in central banking and supervision: building blocks, approaches and techniques

Jens Mehrhoff, Expert, Statistics Department, Deutsche Bundesbank

 

  • The state of the art of Big Data analytics in 2019
  • Overview of building blocks in the fields of data creation, storage, retrieval and analysis
  • Examples of frameworks, approaches and techniques for successful application in the central banking and supervisory environment
  • Tips for effective management of issues preventing a development of a coherent and sustainable Big Data strategy

Wednesday - 22 May 2019

New Tools and Instruments for Macro-prudential and Monetary Policy Making and Analysis

Making the most of Big Data in risk-based reporting

Allan Kearns, Head of Function, Prudential Analytics, Central Bank of Ireland 

  • Key features of effective risk-based supervision frameworks and strategies
  • The impact of Big Data based APIs (Application Programming Interface) and software platforms on regulatory value chain
  • The role of Big Data in market surveillance and risk prediction
  • Tips for effective management of sensitive issues in the areas of security standards and confidentiality

Attention Economics and Big Data

Vincent Hendricks, Professor of Formal Philosophy, University of Copenhagen and Director, Center for Information and Bubble Studies (CIBS)

  • Overview of the current state of the information age
  • Discuss the risks of massive digital misinformation ranging from terrorism to cyber-attacks
  • Assess how big data can undermine democracy and enforce surveillance capitalism
  • Tips for effective management of sensitive issues in an era of misinformation

Identifying core-periphery structures through administrative Big Data: the supervisory data on bilateral interbank exposures case study

Rodrigo Cifuentes, Senior Advisor, Financial Policy Division, Central Bank of Chile

  • Examples of analytical applications of large amounts of granular data from the Internal Revenue Service
  • Overview of tools and frameworks for identifying the structure of a financial network and its transition over time
  • Tips for effective management of methodological issues and challenges
  • Discussion: What are the key features of effective inter-institutional data sharing and cooperation?

Big Data as a facilitator for monetary policy analysis and implementation

Timur Hülagü, Deputy Executive Director, Statistics Department, Central Bank of the Republic of Turkey

  • Case study on how Big Data is utilised for monetary policy-making
  • Examples of Big Data applications for pattern spotting and trend analysis
  • Risk and limitations of Big Data based softwares and analytical platforms
  • Discussion: How will technological innovation affect monetary policy?

The emerging role of Big Data in central banking communication: the social media case study

Per Nymand-Andersen, Adviser, European Central Bank and Bruno Tissot, Head of Statistics & Research Support, Monetary and Economic Department, Bank of International Settlements (invited)

  • Implications and opportunities of disruptive technological innovation for central bank communication
  • Examples of applications in the areas of design, execution and evaluation of different communication strategies and frameworks
  • Assessment of the analytical value of new tools – such as text-mining – based on Big Data and Machine Learning
  • Case study: analysis of social media response to a monetary policy announcement

Thursday - 23 May 2019

Revolutionising Big Data Applications

Making the most of available technology: a case for combining Big Data and Machine Learning

Gareth Peters, Professor for Statistics in Risk, Heriott-Watt University

  • Overview of methods and practices for combining available technologies and digital platforms
  • Management of key operational and ethical risks and challenges
  • Implications for institutional organisation and resources
  • Hands-on exercise: combining Big Data and Machine Learning for systemic risk identification and containment

New opportunities for effective financial crime management

Kimmo Soramäki, Founder and CEO, Financial Network Analytics and founding Editor-in-Chief, Journal of Network Theory in Finance

  • Overview of key financial crime risks of the digital era
  • Examples of Blockchain and Machine Learning applications in KYC and KYCC
  • The role of international and public-private cooperation and coordination
  • Hands-on exercise: automated vs. manual Fraud and AML investigation

The role and potential of Big Data in cyber security and resilience

Roland Wettstein, Head IT Banking Applications, Deputy Director, Swiss National Bank

  • Overview of key cyber risks and challenges in 2019
  • Implications for the work of central bank statisticians, economists and data analysts
  • Applications of Big Data based frameworks, tools and methods for identification and containment of cyber attacks
  • Examples of success stories and steps to be avoided

Friday - 24 May 2019

Operational Arrangements and Institutional Organisation

How to set up a Big Data lab

Markus Trzeciok, Principal Project Manager, European Central Bank

  • Overview of the latest developments in Big Data resourcing in central banks
  • Challenges of building the Big Data infrastructure for sustainable scalability and flexibility
  • Strategies and frameworks for the effective integration of new datasets into policy analysis and decision-making procedures
  • Case study: learn how the European Central Bank embraced Big Data technologies to support better data analysis

 

Using Big Data for nowcasting macro-economic indicators: the ECB-Google case study

Per Nymand-Andersen, Adviser, European Central Bank

  • Overview of emerging trends in regulatory Big Data
  • Opportunities and challenges of Big Data applications for financial stability and systemic risk analysis
  • Examples of good practice in combining Big Data from the internet, administrative and commercial sources
  • Case study: European Central Bank’s use of Google search data in autoregressive now-casting models

Delegate action points and course conclusion

Led by the chair, Per Nymand-Andersen

  • Summary of the training course
  • Discussion of the observed trends and case studies
  • Application of learning points in the delegates’ home organisations
  • Preparation of action points