Advancing central banking through data excellence and AI driven innovation
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.
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 departments: Suptech, cybersecurity, sentiment analysis, market surveillance, fraud transactions, macro-economic forecasting, financial stability, and communication
What does ‘AI-ready data’ look like?
- Leveraging AI on non-traditional data sets
- Integrating AI with in-house analytical tools for reliable data quality and reporting
- Case studies showcasing modernisation journeys by prioritising data management in a world of advanced AI solutions (eg: Gen Al, Agentic AL, etc)
- Techniques to modernise data systems without complete system overhauls
- Approaches to navigate fragmented and siloed data sources and architectures
Strengthening data quality with AI and machine learning
- Addressing challenges in data cleaning, data volume, identifying outliers and heterogeneity
- Strategies for ensuring robust data is available that links new observations and series effectively
- Application of scalable machine learning techniques, such as Isolation Forests, for detecting outliers and anomalies within large datasets
Data governance, accountability and policy implications
- Integrating AI in existing risk management frameworks
- Establishing governance structures and disclosure practices for data quality control and metadata management
- Ensuring effective data protection measures are implemented
- Fostering ethical AI governance to eliminate algorithmic bias
Balancing responsible innovation with regulation
- Continuous learning programs aimed at promoting responsible AI adoption among central bank staff
- Embedding strong AI-ethics throughout the institution
- 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
