Big Data: New Opportunities in Economic and Statistical Analysis 2022

Big Data: New Opportunities in Economic and Statistical Analysis

Big Data: New Opportunities in Economic and Statistical Analysis

May 16 – 19

Live Content sessions held: 9am–1pm (EDT) | 2pm–6pm (BST) | 9pm–1am (SGT) 

Chair: Per Nymand-Andersen, adviser, a European central bank 

Big data and data science are transforming central banking. Vast quantities of data available in near-real time offer decision-makers unparalleled opportunities for analysis.

Big data empowers researchers to explore and understand complex statistical as well as economic challenges. For supervisors advanced data science can deliver an unprecedented view into the financial system.

Yet, transformation is not without cost and risk. New approaches to data are resource intensive and new techniques require care and calibration. The challenge for central bankers is to integrate innovation with existing practices in a way that adds value for policy-makers and stakeholders.

This course is designed to equip central bankers to meet these challenges. Each day will feature three hours of expert-led Live Content to maximise the opportunity to share and learn. The chair will ensure participants have opportunities to network throughout the course, culminating in a workshop on practical implementation.

Read the letter from our course chair.

Agenda

Two weeks prior to your training course you will be emailed access to our content hub with course materials, including a trial to Central Banking if you are not already subscribed. There will be a combination of articles, reports and presentations that will contribute to two hours of preparation time for the live content. Presentations for the sessions will also be held here subject to the speaker approval.

The foundations of big data

14:0014:45

Course introduction
Course introduction session led by the chair

14:00 - 14:30

  • Introductions and welcome from the chair
  • Overview of the training course and key themes
  • Discussion of delegate expectations and particular areas of interest
Per Nymand-Andersen

Adviser

a European central bank

Per Nymand-Andersen has over 25 years of Central Banking Experiences and was part of creating and developing the European Central Bank from scratch. Per has developed his expertise in banking and financial markets, fintech, data science, communications, securities settlement systems, statistics and Management.

Per holds several Fintech/data science Advisor Board positions in private and simi-public organisations. Per is a Lecture at Goethe-Universität Frankfurt and is a frequent speaker at international events and author of several publications/articles regarding financial markets, data science, communication and statistics. His recent renown book “Data science in Economics and Finance for Decision Makers” was published by Riskbooks.com.

Prior to joining the ECB, he provided market research consultancy services for the European Commission, Luxembourg.

Per has an MBA in Economics and Management Science from Copenhagen Business School, Denmark and has a Fintech certificate from Harvard University.

Per speaks four languages (English, German, French and Danish).

Further details: https://www.linkedin.com/in/per-nymand-andersen-81609913

14:4516:00

Overview of new data sources in economics and finance

14:30 - 15:30

  • Big data and central banking – purpose and use
  • Fintech, data never sleeps
  • Quality and transparency of new data sources
Per Nymand-Andersen

Adviser

a European central bank

Per Nymand-Andersen has over 25 years of Central Banking Experiences and was part of creating and developing the European Central Bank from scratch. Per has developed his expertise in banking and financial markets, fintech, data science, communications, securities settlement systems, statistics and Management.

Per holds several Fintech/data science Advisor Board positions in private and simi-public organisations. Per is a Lecture at Goethe-Universität Frankfurt and is a frequent speaker at international events and author of several publications/articles regarding financial markets, data science, communication and statistics. His recent renown book “Data science in Economics and Finance for Decision Makers” was published by Riskbooks.com.

Prior to joining the ECB, he provided market research consultancy services for the European Commission, Luxembourg.

Per has an MBA in Economics and Management Science from Copenhagen Business School, Denmark and has a Fintech certificate from Harvard University.

Per speaks four languages (English, German, French and Danish).

Further details: https://www.linkedin.com/in/per-nymand-andersen-81609913

16:0016:15

Break

15:30 - 15:45

16:1516:55

How to assess trustworthy AI in practice

15:45 - 16:45

  • The ethical and societal implications of artificial intelligence systems 
  • Introduction to a novel process based on applied ethics - Z-Inspection®
  • Assessing if an AI system is trustworthy in practice
Roberto Zicari

Affiliated professor, Yrkeshögskolan Arcada, Helsinki, Finland, and adjunct professor at the Seoul National University, South Korea

Roberto V. Zicari is an affiliated professor at the Yrkeshögskolan Arcada, Helsinki, Finland, and an adjunct professor at the Seoul National University, South Korea.

Roberto V. Zicari is leading  a team of international experts who defined an assessment process for Trustworthy AI, called Z-Inspection®.

Previously he was professor of Database and Information Systems (DBIS) at the Goethe University Frankfurt, Germany, where he founded the Frankfurt Big Data Lab .

He is an internationally recognized expert in the field of Databases and Big Data. His interests also expand to Ethics and AI, Innovation and Entrepreneurship. He is the editor of the ODBMS.org web portal and of the ODBMS Industry Watch Blog.  He was for several years a visiting professor with the Center for Entrepreneurship and Technology within the Department of Industrial Engineering and Operations Research at UC Berkeley (USA).

16:5517:00

Break

16:45 - 17:00

17:0018:00

Machine learning and statistics: variations on a theme

17:00 - 18:00

  • Machine learning and statistics
  • Classifying data-analysis methodologies
  • What are the limits of our predictive capacity?
  • Pitfalls and hidden strengths of machine learning methods
David Bolder

Director of model development and economic capital

Nordic Investment Bank

David Jamieson Bolder is currently Director of Model Development and Economic Capital at the Nordic Investment Bank. Prior to this appointment, he was in charge of the World Bank Group’s model-risk function. Other stops over the years include quantitative analytic roles at the Bank for International Settlements, the Bank of Canada, the World Bank Treasury, and the European Bank for Reconstruction and Development. He has authored numerous papers, articles, and chapters in books on risk-management, financial modelling, stochastic simulation, and optimization. Two comprehensive books--on the topics of fixed-income portfolio analytics and credit-risk modelling--round out his list of publications. His almost 25-year career, by way of high-level summary, has focused on the application of mathematical techniques towards informing decision-making in the areas of sovereign-debt, pension-fund, portfolio-risk, and foreign-reserve management.

Creating a framework for big data

14:0015:00

Making sense of big data

14:00 - 15:00

  • Working with big data
  • Opportunities for central banks
  • Organising big data work
  • Challenges & policy issues with handling and using big data
Bruno Tissot

Head of Statistics and Research Support

Bank for International Settlements

Bruno Tissot is the Head of Statistics and Research Support at the BIS and Head of the Secretariat of the Irving Fisher Committee on Central Bank Statistics (IFC). He has been working at the BIS since 2001, as Senior Economist and Secretary to the Markets Committee of Central Banks in the Monetary and Economic Department and then as the Adviser to the General Manager and Secretary to the BIS Executive Committee. Between 1994 and 2001 he worked for the French Ministry of Finance. He is currently Head of BIS Statistics and Research Support and is a graduate from École Polytechnique (Paris) and of the French Statistical Office INSEE.

15:0015:15

Break

15:00 - 15:15

15:1516:15

Text mining: applications in economic analysis (and text analysis)

15:15 - 16:15

  • Taxonomy of data derived from textual databases
  • Overview of tools and methods for their systematic analysis
  • Examples of applications in predictive models
  • Case study: textual analysis for monitoring macroeconomic developments
Paola Cerchiello

Associate professor

University of Pavia

Paola mainly focuses on methodological statistics and data analysis: she is currently working on text data models, systemic risk, financial technologies (fintech), big data analysis, ordinal variables, spatio-temporal models.  She collaborates with the Bank of Italy on a Big Data analysis project and she was a member of ‘Big Data Group’ at Deutsche Bundesbank, Frankfurt, Germany. She is member of the board of ‘Statistics and Data Science’ group of the Italian Statistics Association. She is associate of the UCL Centre for Blockchain Technology, which is the nucleus for DLT and Blockchain research and engagement across eight different departments at UCL and for its Research and Industry Associate network.

She is Associated researcher of RiskLab (http://risklab.fi/), RiskLab Finland is a research group at Arcada and a research laboratory of Infolytika Ventures, with the objective to study and develop the fields of risk analysis through machine learning and visual analytics. She is advisor of Initial Coin Offering of Blufolio AG, Switzerland.

She has been appointed as Expert and Project Evaluator from the Fund for Scientific Research-FNRS (F.R.S.-FNRS), a funding agency located in Brussels in Belgium and as reviewer of the ‘Science Foundation Ireland - SFI’, in Dublin, Ireland.

She serves as referee for several international journals: The Journal of Royal Statistical Society (Series A), Scientometrics, Expert Systems with Applications, Neurocomputing, European Journal of Operation Research, Statistical Analysis and Data Mining, the Journal of Big data, Small Business Economics Journal, Journal of Classification, Statistical Methods and Applications, WIREs Data Mining and Knowledge Discovery, Information Processing and Management, Social Network Analysis and Mining, Public Management Review, The Journal of Operational Risk, Communication and Statistics, British Journal of Mathematics & Computer Science.

16:1516:45

Networking break
An opportunity to share experiences with your fellow participants.

16:15 - 16:45

16:4517:45

Applying data science in economics and finance

16:45 - 17:45

  • Data science models for large datasets
  • Using predictive models in macroeconomics
  • Case study: working with big data, models, software and examples from Bank of Italy
Juri Marcucci

Economist

Bank of Italy

Juri Marcucci holds a PhD in Economics from the University of California, San Diego. His doctoral thesis was on financial econometrics, under the supervision of Professor Robert Engle (winner of the 2003 Nobel Memorial Prize in Economic Sciences) and it focused on the predictive ability of Regime-Switching GARCH models and common features in volatility. He lectured at the University of Bologna, and Tor Vergata University of Rome and he is currently lecturing a course on “Data-Driven Economics” at the Sapienza University of Rome. He has been visiting scholar at the Federal Reserve Bank of Boston, University of California San Diego and Universitat Pompeu Fabra. He is the organizer of the Italian Summer School of Econometrics on behalf of the Italian Econometric Association (SIDE). He has been the Bank of Italy’s member of the Big Data Committee at the Italian National statistical institute. He has been guest editor of the journals Econometrics and the International Journal of Forecasting. He is co-organizing a series of webinars on Applied Machine Learning, Economics, and Data Science (AMLEDS).

He has worked at the Bank of Italy since 2004 where he has coordinated the task force on Big Data and Machine Learning since 2016. He is now working in the Research Data Center and Innovation Lab in the DG Economics, Statistics and Research. His research interests are on Big Data, Text Mining, forecasting and applied econometrics.

His work appeared in the J. of Econometrics, International J. of Forecasting, Studies in Nonlinear Dynamics & Econometrics, J. of Economics and Business, J. of International Financial Markets, Institution & Money, International Review of Financial Analysis.

Effective use and good governance of big data

14:0015:00

Visualisation: new tools and techniques for visualising new data sets

14:00 - 15:00

  • Why is good data visualisation and storytelling important?
  • Overview of best practice techniques
  • Examples of improved communication of financial data using these techniques and technology
  • Discussion: where are the opportunities for central bank communication?
Ľuboš Perniš

Head of suptech development

FNA

Adam Csabay

Global head of central banking engagement

FNA

Adam Csabay is the Global Head of Central Banking Engagement at FNA. He focuses on technology and innovation projects with central banks and financial authorities. Prior to joining FNA, Adam was Lead Coordinator (Central Banking China Working Group) at Central Banking Publications. He was also a Founder and Manager - Central Banking Fintech & Regtech Advisory Board. Adam graduated with Distinction from the University of Oxford with a degree in Contemporary Chinese Studies - Specialisation in International Relations and Economics. He speaks 5 languages

15:0015:15

Break

15:00 - 15:15

15:1516:15

AI and ML implications for data management and analytics

15:15 - 16:15

  • Current capabilities of AI and machine learning
  • Data management, processing and analysis
  • Examples of machine learning based software solutions for the regulators and the regulated
  • Discussion: what are the best opportunities in AI and machine learning 
Kimmo Soramäki

Founder and CEO

FNA

Kimmo Soramäki 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.

16:1516:45

Networking break
An opportunity to share experiences with your fellow participants.

16:15 - 16:45

16:4517:45

The principles for successful data governance

16:45 - 17:45

  • Opportunities: identify the benefits of data governance for your organisation.
  • Capability: set yourself up for success by ensuring that you have the right resources and knowledge.
  • Custom-build: design a Data Governance Framework which is tailormade to your organisation.
  • Simplicity: avoid complexity and make it easy to embed Data Governance.
  • Launch: implement on an iterative basis and start to see the benefits of your work.
  • Evolve: develop your framework as your organisation evolves to make further gains
Nicola Askham

The Data Governance Coach

Known as The Data Governance Coach, Nicola helps organisations understand and manage their data better.

For almost two decades she’s helped corporates to reduce costs, inefficiencies and to remain competitive. Typically, people turn to her because their data is a mess and they need help unravelling it.

As well as providing coaching and consulting to help her clients better manage their data, Nicola runs three popular training courses every year, because she feels it is important to give people the skills to make sure that data is used to solve problems and make better informed decisions.

Nicola is the leading data governance coach and training provider in the UK. She supports companies with implementing their data governance initiative, so they can sustain it on an on-going basis. She holds a unique level of experience in the Data Governance field, and has experience in training and coaching major organisations to help them implement full data governance frameworks.

Nicola has developed a powerful methodology for implementing data governance based on over 19 years of experience and research into best practices in the field of data governance. Her methodology breaks down the data governance initiative into logical steps, which ensures that businesses design and implement a data governance framework that is right for the organisation.

Nicola initially worked for a leading UK Bank and moved into consultancy at the beginning of 2009.

Nicola is a Director and Committee Member of DAMA UK, she sits on the Expert Panel of Dataqualitypro.com, and regularly writes and presents internationally on data governance best practice.

Techniques to leverage data

14:0015:00

Advanced statistical analysis of large-scale web-based data

15:15 - 16:15

  • Data science methods for big data
  • Case study using social media data
  • Challenges and learnings
Jürgen Pfeffer

Associate Professor

Technical University München

15:0015:15

Break

15:00 - 15:15

15:1516:15

Cloud and data management: innovation and opportunities for central banks

14:00 - 15:00

  • Examples of cloud applications in today's central banking environment, advantages and disadvantages
  • Importance of cloud computing for data science and big data analytics
  • Discussion: what does it take to effectively manage limitations and potential legal and security risks?
Jesús Calderón

Managing director

Maclear Data Solutions

Jesús Calderón advises Canadian and international clients in the financial services and energy industries on the implementation of data-driven solutions for risk management in  banking, insurance, capital markets, and energy trading, as well as anti-money laundering and regulatory activities. Jesús has over twelve years of experience in risk management, internal audit, and fraud investigations, where he has specialized in the application of data science and machine learning methods to optimize risk control activities and examinations.

16:1516:30

Break

16:15 - 16:30

16:3017:15

Closing remarks and delegate action plans
Concluding session led by the chair

16:30 - 17:15

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

Learning outcomes

At the conclusion of the training, participants will be able to:

  • Understand the opportunities and limitations of big data
  • Gain insight into the application of cloud technology in policy-making
  • Identify areas where big data and data science can improve operations
  • Understand the requirements for a framework for data governance
  • Use new tools and techniques for visualising new data sets and networks

Per Nymand-Andersen

Adviser

a European central bank

Per Nymand-Andersen has over 25 years of Central Banking Experiences and was part of creating and developing the European Central Bank from scratch. Per has developed his expertise in banking and financial markets, fintech, data science, communications, securities settlement systems, statistics and Management.

Per holds several Fintech/data science Advisor Board positions in private and simi-public organisations. Per is a Lecture at Goethe-Universität Frankfurt and is a frequent speaker at international events and author of several publications/articles regarding financial markets, data science, communication and statistics. His recent renown book “Data science in Economics and Finance for Decision Makers” was published by Riskbooks.com.

Prior to joining the ECB, he provided market research consultancy services for the European Commission, Luxembourg.

Per has an MBA in Economics and Management Science from Copenhagen Business School, Denmark and has a Fintech certificate from Harvard University.

Per speaks four languages (English, German, French and Danish).

Further details: https://www.linkedin.com/in/per-nymand-andersen-81609913