Integrating Non-traditional Data and AI into Central Banking: A Canadian Perspective

James Chapman, Ajit Desai, Maryam Haghighi and James (Jim) C. MacGee

No 2026-17, Staff Analytical Papers from Bank of Canada

Abstract: Rapid advances in artificial intelligence (AI)—including machine learning, natural language processing, and generative AI—are expanding the ability to extract meaningful insights from non-traditional data sources such as text, speeches, images, and real-time transactions, thereby strengthening policy analysis and operational decision-making. These tools also enable more sophisticated analytical approaches to the study of economic dynamics while creating opportunities to improve efficiency across institutional processes and operations. This paper documents the growing use of non-traditional data and AI at the Bank of Canada and their contribution to deeper insight and operational effectiveness. The experience highlights critical considerations for accelerating the responsible integration of AI into central banking functions, including evolving ways of working and career paths, fostering a robust ecosystem for innovation, and addressing emerging risks. A successful AI strategy must balance innovation with trust, transparency, security, reproducibility, sound model governance, data residency, and effective operational risk management.

Keywords: Financial system; Financial stability and systemic risk; Monetary policy; Monetary policy tools and implementation; Money and payments; Payment and financial market infrastructures (search for similar items in EconPapers)
JEL-codes: C45 C55 C88 L23 M15 O33 (search for similar items in EconPapers)
Date: 2026-05
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocsap:26-17

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