2020 Big Data: New Opportunities in Economic and Statistical Analysis
Big Data: New Opportunities in Economic and Statistical Analysis
Agenda
Participants will have access to pre-recorded presentations two weeks before the course
Machine Learning and AI: utilizing Big Data
Pre-recorded presentation
- 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
- Combining Big Data and Machine Learning for systemic risk identification and containment
David Jamieson Bolder is currently head of the World Bank Group's (WBG) model-risk function. Prior to this appointment, he provided analytic support to the Bank for International Settlements' (BIS) treasury and asset-management functions and worked in quantitative roles at 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 financial modelling, stochastic simulation, and optimization. He has also published a comprehensive book on fixed-income portfolio analytics. His career 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.
The building blocks of Big Data
Pre-recorded presentation
- New applications of open source technologies in data collection, management and analysis
- Overview of key models and analytics frameworks
- Tips for management of sensitive issues in the areas of security standards and confidentiality
Data analytics for AML/CFT: AI and Machine Learning in focus
Pre-recorded presentation
- State of the art in AI and Machine Learning
- Key opportunities for AML and CFT
- Implications for data management, processing and analysis
- Examples of Machine Learning based software solutions for the regulators and the regulated
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.
What is new in Big Data and how can we make sense of it?
Pre-recorded presentation
- 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?
Vincent Hendricks
Professor of Formal Philosophy and Director, Centre for Information and Bubble Studies (CIBS)
University of Copenhagen
Times in GMT
11:00 – 11:30
Course introduction
Opening remarks with the course chair
- Introduction of the chairman
- Overview of the training course
- Discussion of the delegate expectations
11:30 – 12:30
Using Big Data for now casting macro-economic indicators
Presentation followed by Q&A
- 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
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
12:30 – 12:45
Break
12:45 – 13:45
Cloud and data management: innovation and opportunities for central banks
Presentation followed by Q&A
- A (very) brief history of cloud computing in central banks
- Standout examples of cloud applications in today’s central banking environment
- Understanding cyber resilience when using cloud
- Discussion: What does it take to effectively manage limitations and potential legal and security risks?
13:45 – 14:00
Break
14:00 – 15:00
Good governance: how should central banks manage Big Data
Presentation followed by Q&A
- Key approaches and challenges to successful governance of Big Data
- Framework for effective management of Big Data
- Strategies to balance oversight and promote innovation
- Discussion: How effective is existing data governance in coping with Big Data in delegates home jurisdictions?
15:00 – 15:00
End of day 1
Times in GMT
11:00 – 12:00
Designing a supervision and surveillance analytics framework
- Key features of effective risk-based supervision in 2020
- Technological foundations of surveillance analytics
- Applications in risk assessment and stress-testing programmes
- Tips for overcoming data collection and processing issues
Allan Kearns is head of analytics within the Insurance Supervision Directorate at the Central Bank of Ireland (CBI). In this role, he is responsible for the establishment of a new analytics function as part of the Solvency II change programme within the central bank. The vision for this function is to extract insights from the new Solvency II Pillar 3 reporting to enhance data-driven supervisory and financial stability decision making. Prior to this role, Allan was a founding member of the CBI's risk management division, with responsibility as a deputy chief risk officer for both financial and non-financial risk management frameworks. Allan has an MSc Economics (London School of Economics), an MSc Risk Management (University College Dublin) and a doctorate from Trinity College (Dublin). He lectures on risk management topics domestically and internationally, including governance and culture, and has published on a broad range of central banking topics.
12:00 – 12:15
Break
12:15 – 13:15
Data Visualisation & Storytelling: techniques and technology to revolutionise your communication
Presentation followed by Q&A
- Opportunities for central bank communications
- Turning from old fashioned to state of the art
- Applications for financial and economic data presentation & storytelling
13:15 – 13:30
Break
13:30 – 14:30
Making the most of open data
Presentation followed by Q&A
- Overview of the benefits of open data
- Strategies and benefits of linking data with third parties
- Examples of making data more disseminating and consumable
- Workshop: visualizing how data is being used
Eoin MacCuirc works for the Irish, Central Statistics Office (CSO) in Cork since 1994. Eoin works in Business Liaison and Quality Assurance, Technology Division and has responsibility for the coordination and management of Electronic Data Capture on Mobile Devices, the CSO Data Management System, rolling out Agile Project Management and Design Thinking across Technology Division and standardising the project management and governance of Technology Division projects.
Eoin has performed many roles in the CSO at all stages of the statistical production process. Eoin sits on many local, national and international working groups and present regularly at conferences at home and abroad.
Eoin sits on the National Public Bodies Advisory Group on Open Data, is a member of the ESS Open Data Committee and Working Group on High Value Datasets, a member of the Smart Dublin Advisory Network and Cork Smart Gateway and Programmable City Groups. Eoin was on the Eurostat DIGICOM steering group a cross cutting project on digital communication, user analytics and innovative products in the European Statistical System. Eoin led the CSO participation in the ESSnet on Linked Open Data to maximise the openness, linkability, analytical and machine accessibility of ESS statistical data. Eoin coordinated and managed the delivery of the ESS IT Security Framework throughout CSO. Eoin lectures in the Institute of Public Administration Diploma course in Official Statistics.
Besides his involvement in the traditional data analytics used in gathering, collating and publishing statistics Eoin has an interest in empowering expert and non-expert users to engage with and analyse statistical data in its many formats. Eoin led the CSO involvement in the Open Cube Project and a UNSD collaboration on spatially representing and visualising SDG indicators. Eoin is particularly interested in how machine readable data, web services and linked data formats will facilitate data analytics across data domains into the future.
Eoin studied Business and Social Studies in Trinity College, Dublin and is a trained project manager and IT System analyst.
Eoin is a native English speaker with basic communication skills in French.
14:30 – 14:45
Break
14:45 – 15:15
Networking break
Post training survey and feedback
CPE/CPD certificate
Course alumni group
Chair
Per Nymand-Andersen
Emeritus adviser
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
By the end of the training course, participants will have gained new knowledge and a more comprehensive understanding of:
- New opportunities in cloud and advanced data analytics
- Using big data for nowcasting macro-economic indicators
- Making the most of big data in risk-based reporting
- Big data governance and impact institutional organisations
- Big data as a tool for monetary policy analysis and implementation
- Matching analytical capabilities with a supervisory framework
- Combining big data analytics and machine learning
