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

Big Data in RegTech and SupTech

Beju Shah, Head of Data Collection and Publication, Bank of England (invited)

 

  • The role of  Big Data as a pillar of regulatory and supervisory technology
  • Applications in the areas of automation and standardisation of data reporting, and closing of the gap between regulatory intention and interpretation
  • Implications for cooperation and coordination between the regulator and the regulated
  • Examples of tasks, functions and frameworks where Big Data analytics needs to be substantiated with other RegTech and SupTech tools and techniques

Making the most of Big Data in risk-based reporting

Allan Kearns, Head of Function, Prudential Analytics, Central Bank of Ireland (invited)

 

  • 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

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

Identifying core-periphery structures through administrative Big Data: the Internal Revenue Service 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?

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

TBC

 

  • 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

The emerging role of the Chief Data Officer (CDO)

David Hardoon, Chief Data Officer, Monetary Authority of Singapore (invited)

 

  • Overview of motivations behind the position of the Chief Data Officer
  • Examples of key roles and responsibilities
  • Implications for resourcing and institutional organisation
  • Discussion: What are the key expectations from and to the CDO?

 

Enabling and resourcing the Big Data function

Christopher Kurz, Group Manager, Research and Statistics Division, Federal Reserve Board of Governors (invited)

 

  • Examples of frameworks and indicators to assess suitability and needs of the Big Data function
  • Implications for institutional strategies and inter-departmental cooperation
  • Tips for selection and coordination of in-house technology solutions with the outsourced ones
  • Discussion: What does it take to attract, train and maintain the best talent?

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