Big Data and Data Science: Advanced Analytics in Economics and Finance
Big Data
Big Data and Data Science: Advanced Analytics in Economics and Finance
New Tools and Frameworks for Supervisory and Regulatory Analysis
09:00 – 10:30
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.
10:30 – 11:00
Coffee break
11:00 – 12:30
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.
12:30 – 13:30
Break
13:30 – 15:00
Visualisation: new tools and techniques for visualising new data sets
- Data visualization: state of the art
- Overview of machine-learning based tools and techniques
- Applications in financial data analytics
- Opportunities for central banks’ communications
15:00 – 15:30
Coffee break
15:30 – 17:00
Text mining: applications in economic analysis
- 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
17:00 – 17:15
Coffee break
17:15 – 18:45
Agent-based modelling in practice
- The state of the art of agent based modelling in 2020
- Strategies to overcome design challenges
- Implications for institutional organisation and resources
- Hands on exercise: how to build a successful agent-based model
Making the Most out of Big Data (Lab Workshops)
09:00 – 10:30
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
10:30 – 10:45
Coffee break
10:45 – 12:15
Applying Big Data techniques to a central credit register: turning micro into macro
- New Taxonomy of central credit register data
- Applications in detecting risk build-up and concentration
- Examples of new Big Data based techniques and methods
- Case study: building a new central credit register framework
12:15 – 13:15
Break
13:15 – 14:45
New techniques for AML and financial crime detection
- 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
Towards Good Governance and Successful Outreach
09:00 – 10:30
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?
10:30 – 11:00
Coffee break
11:00 – 12:30
Rightsourcing: an interdisciplinary approach to setting up a Big Data lab
Pre-recorded presentation
- Overview of the latest developments in Big Data resourcing in central banks
- Challenges of building 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: how did the Bank of England embrace Big Data technologies to support better data analysis
12:30 – 13:00
Networking break
13:00 – 14:00
Closing remarks and delegate action plans
Concluding session led by the chair
- 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