'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. Looking at the latest role of AI on core banking mandates, and its transformative role in applying across departments such as payments, risk management, financial stability, and cybersecurity. The course will address the importance of AI-ready data, enhancing data quality through machine learning techniques, and establishing robust governance frameworks. Additionally, it emphasizes 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.
Maxime-Edouard Laloire
Head of strategy, organization and innovation management
National Bank of Belgium
Jeffrey Durieux
Enterprise IT architect
National Bank of Belgium
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
- Head of Suptech
- Head of Regtech
- Head of Innovation
- Head of AI
- Chief Fintech Officer
- Head/Director of Information Technology
- Machine Learning Engineers
- IT Architecture
- Principal Solution Architect
- Digital Transformation Leads
- Head of Supervision
- Head of Regulation
- Chief Legal Officers
- Policy Advisors
