Data Science and Innovation: Steering Central Banks Statistics Towards Agile Governance
Conference
Category: International Statistical Institute
Proposal Description
The accessibility to larger and complex big data sources and the use of sophisticated techniques based on machine learning open up new possibilities for central bank statisticians. This session focuses on the implications for developing new business-led initiatives within the information architecture of central banks. The staring point is the realisation of the need to closely align IT strategies directly with business objectives. In turn, digital transformation offer a wide range of possibilities to accelerate innovation, enhance operational efficiency, and improve decision-making processes.
Yet success will depend on carefully reviewing recent successes and challenges of implementing business-led IT frameworks in central bank statistical departments and more generally in the various institutions involved in official statistics. In addition, attention should focus on analysing the various strategies that can be deployed by central banks to foster a culture of continuous improvement and digital excellence. Lastly, the session will provide an opportunity to review the tools, governance models, and collaborative practices that can be driving these changes effectively, helping central banks to navigate in the digital transformation journey.
Submissions
- Compilation of Cash Balance of Payments Statistics: Methodology and Experience in Overcoming Challenges in Malaysia’s Context
- Enhancing anomaly detection in financial markets with an LLM-based multi-agent framework
- Leveraging generative AI for official statistics: opportunities, challenges and use cases
- Review of Basic Principles for Statistics at the Bank of Japan