Unlocking cutting edge innovations for central banks: The key to data and emerging technologies
Data Q4 2024
Chair and speakers
Maryam Haghighi
Director, data science
Bank of Canada
Maryam Haghighi is the Director of Data Science at the Bank of Canada, where she provides strategic leadership for data science, including artificial intelligence and machine learning, in support of Canada’s central bank’s mandate.
She has successfully led diverse teams, from academic environments to global enterprises and private partnerships, to solve complex data problems for multi-billion-dollar projects on the ground and in the skies.
Prior to joining the Bank, she pioneered the first ever space-based data analytics system of a new satellite constellation. As Canada’s delegate to international panels under the United Nations, she provided data and modelling expertise for the global aviation risk management.
Previously, she successfully applied her expertise to a diverse range of projects within the Government of Canada, in health care and in not-for-profits.
She holds a doctorate in Mathematics and a Master of Science in Applied Mathematics from the University of Ottawa. She was a professor of mathematics and computer science courses and has been a technical consultant in patent processes. She has a deep passion for interdisciplinary work as well as supporting women in technology initiatives.
Marina M. Tavares
Senior economist
IMF
Marina M. Tavares is a senior economist in the Climate Change Structural Reforms Division of the IMF’s Research Department. Before joining RES, Marina led the working group on the interconnections between macroeconomic policy and inequality under FCDO-IMF Collaboration. Her research interests include macroeconomics, labor markets, AI, climate change, gender, and inequality. Before joining the Fund, Ms. Tavares worked as an assistant professor at Instituto Tecnologico Autonomo de Mexico (ITAM), and she holds a Ph.D. in Economics from the University of Minnesota.
Agenda
13:00 – 13:15
Chair’s opening remarks
- Introduce themselves and the key learning outcomes to the delegates
- Provide an overview of the course
- Encourage delegates to keep their cameras on and ask questions
Maryam Haghighi is the Director of Data Science at the Bank of Canada, where she provides strategic leadership for data science, including artificial intelligence and machine learning, in support of Canada’s central bank’s mandate.
She has successfully led diverse teams, from academic environments to global enterprises and private partnerships, to solve complex data problems for multi-billion-dollar projects on the ground and in the skies.
Prior to joining the Bank, she pioneered the first ever space-based data analytics system of a new satellite constellation. As Canada’s delegate to international panels under the United Nations, she provided data and modelling expertise for the global aviation risk management.
Previously, she successfully applied her expertise to a diverse range of projects within the Government of Canada, in health care and in not-for-profits.
She holds a doctorate in Mathematics and a Master of Science in Applied Mathematics from the University of Ottawa. She was a professor of mathematics and computer science courses and has been a technical consultant in patent processes. She has a deep passion for interdisciplinary work as well as supporting women in technology initiatives.
13:15 – 14:15
Case Study: How has machine learning impacted central banks, and what have we learned so far?
- An overview of machine learning, and how it can and has been implemented in different departments within a central bank (I.E. AML /CFT, Climate predictions, economic predictions)
- How to Identify areas within a central bank where AI can be useful, and how to introduce it with a central bank’s infrastructure
- How to identify what model of AI will be suitable for different projects
- Compare machine learning and generative AI, and their uses
14:15 – 15:00
Exploring distributed ledger technologies (DLTs): A new era for data and payments
- Compare traditional and digital finance, and explain DLTs introduction to digital finance
- What is the potential of DLTs for central banks, and why are they growing in popularity?
- How can we use DLTs to connect to traditional finance
- Explore the progress of stage 1 vs stage 2 DLTs
- How will DLTs impact cybersecurity risks, and what are the potential AML concerns with this technology?
15:00 – 15:15
Coffee break
15:15 – 16:00
Preparing central banks for machine learning’s emerging influence on economic output and inflation
- Discuss how automating tasks is impacting the productivity of the current economy
- Will these new technologies impact economic inequality?
- How is artificial intelligence expected to influence workers in the next 5-10 years?
- Evaluate the proposed inflationary and disinflationary effects of automating tasks and machine learning
Marina M. Tavares is a senior economist in the Climate Change Structural Reforms Division of the IMF’s Research Department. Before joining RES, Marina led the working group on the interconnections between macroeconomic policy and inequality under FCDO-IMF Collaboration. Her research interests include macroeconomics, labor markets, AI, climate change, gender, and inequality. Before joining the Fund, Ms. Tavares worked as an assistant professor at Instituto Tecnologico Autonomo de Mexico (ITAM), and she holds a Ph.D. in Economics from the University of Minnesota.
13:00 – 14:00
The possibilities of open finance: The benefits of APIs, data sharing and the potential risks of interconnectivity
- Overview of open banking, open finance, and open data and how central banks are broaching this new stage of data sharing
- Evaluate the current state of open finance regulation, and ask how will open finance influence financial stability?
- How does data sharing coincide with data privacy and protection, what are the current risks of data scraping?
14:00 – 14:45
Case study: Using open-sourced data to predict economic trends and influence monetary policy
- Discuss the importance of using accurate, real-time data for artificial intelligence to make predictions
- Evaluate how central banks are using AI to predict inflation rates and make monetary policy decisions
- How can automating economic forecasts eliminate human error in policy?
- Discuss if the benefits of using AI this way is greater than ethical any and cybersecurity concerns
14:45 – 15:00
Coffee break
15:00 – 16:00
Decrypting the challenges quantum computing brings for a central bank’s security
- Provide an overview of quantum computing, and how it has become a topic of interests for central banks
- As quantum computers gain strength, central bank’s must strengthen their security
- Look at building quantum resistant cryptography
- Discuss the risks to financial stability when encrypted data has already been harvested and is ready for decryption once quantum computing advances
13:00 – 14:00
Cloud computing: Using offsite data storage to increase infrastructure and encourage data sharing
- Case study with a general overview of using cloud computing to support a central bank in places with less infrastructure
- Benefits of having shared data in a central bank
- Evaluate how to protect data while incorporating cloud computing
- Weigh differences of onsite vs offsite data storage risks
14:00 – 14:15
Coffee break
14:15 – 15:00
Navigating the regulation of general-purpose technologies (GPTs) in finance and its implications for future use
- Review of the GPTs (i.e. GenAI) available, and the potential security risks they pose
- What is the endgame of AI and open banking regulations?
- Will GPTs pose the risk of pro-cyclicality?
- Evaluate the role central banks will play in regulating this technology
- What efforts can be put forth to build familiarity with new technologies and AI literacy?
15:00 – 15:45
Chair lead working group
- The chair provides a brief overview of what the course
- The chair presents the delegates with topics
- Delegates engage with discussions based on what they have learned
- Delegates create action plan
Maryam Haghighi is the Director of Data Science at the Bank of Canada, where she provides strategic leadership for data science, including artificial intelligence and machine learning, in support of Canada’s central bank’s mandate.
She has successfully led diverse teams, from academic environments to global enterprises and private partnerships, to solve complex data problems for multi-billion-dollar projects on the ground and in the skies.
Prior to joining the Bank, she pioneered the first ever space-based data analytics system of a new satellite constellation. As Canada’s delegate to international panels under the United Nations, she provided data and modelling expertise for the global aviation risk management.
Previously, she successfully applied her expertise to a diverse range of projects within the Government of Canada, in health care and in not-for-profits.
She holds a doctorate in Mathematics and a Master of Science in Applied Mathematics from the University of Ottawa. She was a professor of mathematics and computer science courses and has been a technical consultant in patent processes. She has a deep passion for interdisciplinary work as well as supporting women in technology initiatives.
15:45 – 16:00
Chair closing remarks
- The chair closes out the course with what they have learned
- provides feedback on discussions brought up by delegate over the course