Big Data and Data Science: New Opportunities in Reporting, Forecasting and Policymaking 2023

Big Data and Data Science: New Opportunities in Reporting, Forecasting and Policymaking 2023

Big Data and Data Science: New Opportunities in Reporting, Forecasting and Policymaking

March 13-16

Live Content Sessions held:  8am-12pm (EST) | 1pm-5pm (GMT) | 9pm-1am (SGT)

Chair: Per Nymand-Andersen, major European central bank

  • Where can central banks effectively deploy big data, machine learning and AI techniques in policymaking?
  • How can central banks securely utilise their data storage needs with the Cloud?
  • What is emerging good practice for data governance?

Data science is transforming the world of economics and finance. And central banks are part of the revolution. In 2023, central banks are tapping into real-time data and accelerating data-based decision-making. They are developing cloud solutions. They are applying machine learning in supervisory activities. But how can they integrate these with existing practices? And what data management techniques can throw new light on financial actors and instruments? This course is designed to equip central bankers to meet these challenges through a data science approach, it will provide a theoretical and practical understanding of modern data management, machine learning applications, and more advanced solutions.

Each day will feature three hours of expert-led Live Content to ensure a range of perspectives on key topics. The course chair will ensure participants have opportunities to network throughout the course, with time set aside for a workshop on implementing key learnings, to maximise the opportunities to share and learn.

Big Data + Data Science Agenda 2023

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.

13:0013:30

Big Data and Data Science: Course introduction
Course introduction session led by the chair

13:00 - 13:30

  • Introductions and welcome from the chairperson
  • Overview of the training course
  • Discussion of the delegate expectations
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

13:3014:15

Overview of new data sources in economics and finance
Overview of new data sources in economics and finance

14:15 - 15:00

  • Big Data and central banking – purpose and use
  • How central banks think about data & statistics
  • Fintech, defiance and social media data, which 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

14:1515:00

Central banking collaborations on data science/big data
Central banking collaborations on data science/big data

13:30 - 14:15

  • Big data and central banks
  • Opportunities
  • Organising 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

12:45 - 13:00

15:1516:00

Strengthening your data science lab and governance
Strengthening your data science lab and governance

15:15 - 16:00

Dr. Thomas Gottron

Principal Data Science Expert

ECB

16:0016:15

Networking break

15:30 - 16:00

16:1517:00

Overcoming the main challenges of collecting and making sense of big data
Overcoming the main challenges of collecting and making sense of big data

16:15 - 17:00

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

Senior economic advisor and economist

Federal Reserve Bank of Philadelphia

Julapa Jagtiani joined the Federal Reserve Bank of Philadelphia as a special advisor in the Supervision, Regulation, and Credit Department in 2008. In this role, she has conducted research and participated in or led several supervisory policy and implementation projects, including CCAR stress testing, recovery and resolution plans, and Basel II qualification reviews for large and complex financial institutions, with a focus on the use of quantitative methods and models for risk management. She is also a fellow member of the Wharton Financial Institutions Center and a Central Bank Research Fellow at the Bank for International Settlements. A career highlight has been contributing to the discussion around the potential impacts of fintech and considerations for shaping future fintech regulations that protect consumers and encourage innovation. 

Previously, Jagtiani was a senior economist at the Federal Reserve Banks of Kansas City and Chicago. Before joining the Federal Reserve System in 1998, she was associate professor of finance at Baruch College and assistant professor of finance at Syracuse University.

Jagtiani has made significant contributions in the fields of financial institutions, financial markets, and bank supervision and regulation. She publishes research in top finance journals, organizes conferences to connect regulators with academics and industry leaders, and is a frequent speaker at conferences and forums. Her research areas include banking policy-related issues, including too big to fail, systemic risk and financial stability, Basel II capital regulations, mergers and acquisitions, mortgages and home equity issues, and credit risk models and management. Her more recent research has focused on issues related to fintech, use of alternative data and AI/ML in credit decisions, small business lending, and community bank mergers.

Jagtiani has been active in the community and has served on the board of directors and finance committees at various organizations, including the Leadership Council Board of Directors for the American Red Cross, the Center for Practical Bioethics, and the Parents Council at Johns Hopkins University; she also belongs to the Union League of Philadelphia. Jagtiani has a Ph.D. in finance and an M.B.A. from New York University’s Stern School of Business, where she held the Rockefeller Foundation Fellowship.

 

13:0014:00

The model universe of machine learning and statistics
The model universe of machine learning and statistics

13:00 - 14:00

  • Big Data versus Machine Learning
  • Introduction to Neural Networks 
  • Optimization and Regularization  
  • Early Applications of Machine Learning in Finance  
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.

14:0014:15

Break

12:45 - 13:00

14:1515:15

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

11:00 - 11:45

  • Taxonomy of methods for textual analytics
  • 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.

15:1515:30

Networking break

15:30 - 16:00

15:3016:30

Applying data science in economics and forecasting demand
Applying data science in economics and forecasting demand

15:30 - 16:30

  • Data science models for large datasets
  • Using predictive models in macroeconomics
  • Case study: working with big data, models, software and in-house examples
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.

13:0014:00

Reporting frameworks: using data management frameworks for risk-based reporting
Reporting frameworks: using data management frameworks for risk-based reporting

13:00 - 14:00

  • Strategies for extracting, standardizing, and consolidating analytical data
  • How to develop regulatory requirements for better transparency
  • How can data quality be improved for the future?
Fabiola Herrera

Deputy Manager Systems and Innovation

Central Bank of the Dominican Republic

14:0014:15

Break

12:45 - 13:00

14:1515:15

AI and ML implications - unlocking the potential of big data and AI
AI and ML implications - unlocking the potential of big data and AI

14:15 - 15:15

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

Chief Product and Technology Officer

FNA

15:1515:30

Networking break

15:30 - 16:00

15:3016:30

The use of big data tools in forecasting demand
The use of big data tools in forecasting demand

15:30 - 16:30

  • Advanced statistical analysis of large-scale web-based data
  • Data science methods for big data
  • Case study using social media data
  • Challenges and learnings
Jürgen Pfeffer

Associate Professor

Technical University München

13:0013:45

Case study: use of Alternative data sources: the added value of credit card data
Case study: use of Alternative data sources: the added value of credit card data

13:00 - 13:45

  • Alternative data sources and why they supplement official statistics
  • Nowcasting using alternative data
  • Supplementary insights and tracking trends and turning points
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

13:4514:30

Machine learning and AI in banking supervision

13:45 - 14:30

Michael Berns

Director - AI and Fintech

PwC

Michael is a Director at PwC where he leads the AI and FinTech Practice. He is an AI Thought Leader & FinTech Veteran with 17 years of international experience having run client engagements on five continents. 

He spent more than a decade leading engagements with Fortune 500 companies before then disrupting that space with innovative solutions after his Executive MBA at London Business School. 

Michael has a broad background across blue chip names such as Morgan Stanley & Moody’s as well as a range of smaller innovative AI Firms.

Aside from his day job he also takes a keen interest in understanding the latest in innovation by helping AI firms to scale and has been a Mentor and Judge for organisations like Startup Bootcamp, Virgin Money Startup, Cocoon Network, Level 39 and MIT IIC for the last 9 years.

As a well-known Expert in his field, Michael acts as keynote speaker at international conferences and guest lecturer at a number of business schools including London Business School, Mannheim Business School, IMC and others. 

His “AI in Financial Services” PwC study (with 150 participating experts) was released on May 11 2020 and the “AI Book” to which he contributed as Co-Author was released in print on May 13 2020. 

Many of Michael’s other articles, past speaking engagements and contributions in regards to AI & FinTech are available via his LinkedIn profile.

14:3015:15

Managing Big Data - a case study

14:30 - 15:15

  • Designing a Big Data Platform
  • Challenges in embedding a Big Data Platform
  • The Bank of England's long term strategy for Big Data 
Rebekah O'Toole

Data & Analytics Platform, Strategy & Implementation Manager

Bank of England

Rebekah O'Toole heads up the Bank of England’s Big Data Platform service.  She led a multi-disciplinary team in the detailed design of the operating model for the Big Data Platform, and has overseen its implementation into a live operational service. 

Prior to her appointment at the Bank of England, she was a Director at a Consultancy with over 20 years' experience in delivering large/complex transformational change programmes. Leading engagements with FTSE100 companies and central and local government.

Rebekah has an MBA and specialises in business change management.

Clare Bullivant

Data & Analytics Platform Delivery Manager

Bank of England

Clare is the Delivery Manager for the Bank of England's big data platform. Clare leads a team of Delivery Partners to ingest data onto the Bank’s new Data & Analytics Platform, working with users to define requirements and oversee all aspects of the on-boarding journey. Clare also helps to deliver continuous improvements in user experience & platform functionality, as well as implementing the relevant governance processes to ensure proper usage of the platform.

Prior to her current role, Clare was a key member responsible for leading the design and delivery of the Bank’s first big data platform and the new platform that has since gone into production. Clare started her career as a junior programmer developing the Bank’s Real Time Gross Settlement service, the infrastructure that holds accounts for banks, building societies and other institutions. During her career Clare has worked her way up to Senior Tech lead roles before settling into a Delivery Manager role.

Clare has over 20 years of experience in delivering technology solutions to enable the Bank of England's business users to derive better insights from their data.

15:1515:30

Networking break

15:30 - 16:00

15:3016:15

Big Data and Data Science: closing remarks + delegate action plans
Closing remarks and delegate action plans

15:30 - 16:15

  • 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
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

Learning outcomes

  • Understand current applications of technologies like machine learning, AI and text mining
  • Identify areas where data science can improve operations, forecasting and policy making
  • Gain insight into the inclusion of cloud technology in existing architecture
  • Understand the requirements for a framework for data governance
  • Use new tools and techniques for visualising new data sets and networks