'Trusted AI' for central banks
About the course
Participants will gain essential insights into the integration of artificial intelligence (AI) and data management for central banks. This course will consider the latest role of AI on core central banking mandates, and its transformative role across departments such as payments, risk management, financial stability, and cybersecurity. It will address the importance of AI-ready data, enhancing data quality through machine learning techniques, and establishing robust governance frameworks. Additionally, the course will emphasize the necessity of fostering ethical AI practices and promoting responsible innovation, ensuring central banks are well-prepared to navigate the evolving landscape of digital transformation in finance.
Tutors
Eva Morin
AI Expert
European Central Bank (ECB)
Eva Morin is currently working as an AI expert within the SupTech function at the European Central Bank (ECB), where I contribute to the development and deployment of data-driven and machine learning solutions to enhance supervisory processes. Previously, she worked as a supervisor in vertical supervision, which gives me both an operational and strategic perspective on the use of AI in supervision.
Jungphil (JP) Park
Director of the digital innovation office and chief data officer
Bank of Korea
Jungphil Park is the Director of the Digital Innovation Office and Chief Data Officer at the Bank of Korea, where he oversees the formulation and implementation of the Bank’s data and digital strategies. He spearheads the BOKI (Bank of Korea Intelligence) project, deploying an enterprise-wide Large Language Model (LLM) platform on a private cloud infrastructure to embed generative AI across the organisation.
Earlier in his career, Mr Park worked extensively with global standard-setting bodies, including the Bank for International Settlements (BIS), the Basel Committee on Banking Supervision (BCBS), and the Financial Stability Board (FSB). He represented the Bank of Korea in several BCBS working groups, contributing to the design of post-Global Financial Crisis (GFC) regulatory frameworks. He was also seconded to the Prudential Regulation Authority (PRA) at the Bank of England, where he assessed the policy implications of prudential regulations and their impact on market competition.
Agenda
Welcome and context setting
- Introduction and setting objectives for the training program
AI is already affecting core central banking mandates
- Overview of the AI landscape
- Central banks as ‘informed observers’ and ‘users’ of AI
- Applications of AI across supervision and regulation
Building scalable AI models for central banks
- How to start? The importance of identifying the right use cases for AI
- From concept to practice: AI solutions to enhance core central banking activities
- AI architecture and design: building scalable, sustainable and resilient foundations in an increasingly complex functional and technological landscape
- Overcoming and addressing silos: fragmented skills and scarce resources through flexible governance
- Building and fostering an internal culture of innovation while supporting the effective deployment of AI across the organisation
AI governance, risk management, and cooperation
- Balancing innovation with regulation: integrating AI in existing risk management frameworks
- Establishing governance structures and disclosure practices for data quality control and data management
- Policy considerations for AI implementation
- Continuous learning programs aimed at promoting responsible AI adoption among central bank staff
- Collaborating with central banks and regulators through domestic and international forums and working groups
Learning objectives
- Understand the current impact of artificial intelligence on central banking mandates and identify relevant applications across different departments.
- Define the characteristics of “AI-ready” data and explore techniques for integrating AI with internal analytical tools while ensuring data quality.
- Develop strategies to enhance data quality using AI and machine learning, including overcoming challenges in data cleaning, management, and integration.
- Establish governance structures for responsible data management and policy implications, while integrating ethical guidelines into AI use.
- Promote a culture of continuous learning to ensure responsible AI adoption within central banks, collaborating with diverse stakeholders.
Who should attend
- Heads of Data
- Chief Data Officers
- Heads of Suptech
- Heads of Regtech
- Heads of Innovation
- Heads of AI
- Chief Fintech Officers
- Heads of Information Technology
- Machine Learning Engineers
- IT Architects
- Principal Solution Architects
- Digital Transformation Leads
- Heads of Supervision
- Heads of Regulation
- Chief Legal Officers
- Policy Advisors
