AI and governance anchor three-year central bank data strategy
Opening the 13th Biennial Conference of the Irving Fisher Committee in Basel, officials presented a three-year strategy focused on AI adoption, modernized statistics and data governance across central banks.
A three-pillar roadmap for future data
The Irving Fisher Committee on Central Bank Statistics introduced a three-year strategy built around three core priorities: modernising statistical production, accelerating artificial intelligence adoption, and strengthening data governance frameworks.
A recent membership survey identified AI as the highest innovation priority for central banks, driving practical use cases in information retrieval, supervisory analysis and statistical production.
To streamline international collaboration, the committee has integrated the Central Bank Data Collaboration Group under its umbrella, creating a unified network for data leaders.
Ongoing work also targets granular microdata standards, governance guardrails for trustworthy algorithms, and metrics to close climate-related data gaps.
Broadening a thirty-year mandate
Established nearly 30 years ago, the committee revised its terms of reference in 2025 to formally expand beyond official statistics into data science and technology.
Central banks face heightened economic uncertainty that requires faster, non-traditional inputs such as payments records, corporate disclosures and satellite imagery.
The conference emphasized that integrating novel data streams requires strict quality control, recalling Irving Fisher's warning that “mere empiricism is seldom very fruitful.”
Right direction, slow machinery
Central banks must urgently modernize their data infrastructure to navigate real-time economic volatility.
Yet relying on voluntary committee coordination risks producing non-binding talk instead of actionable standards.
Without unified governance guardrails, rapid AI deployment could undermine statistical credibility.