Integrating Non-traditional Data and AI into Central Banking: A Canadian Perspective Staff analytical paper 2026-17 James Chapman, Ajit Desai, Maryam Haghighi, James (Jim) C. MacGee This paper reviews how central banks are integrating non traditional data and artificial intelligence (AI) into policy analysis and operations. Using the Bank of Canada’s experience, it examines emerging applications, governance challenges, and strategic choices for responsibly scaling AI to enhance insight, efficiency, and institutional resilience. Content Type(s): Staff research, Staff analytical paper JEL Code(s): C, C4, C45, C5, C55, C8, C88, L, L2, L23, M, M1, M15, O, O3, O33 Research Theme(s): Financial system, Financial stability and systemic risk, Monetary policy, Monetary policy tools and implementation, Money and payments, Payment and financial market infrastructures
Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems Staff working paper 2024-15 Ajit Desai, Anneke Kosse, Jacob Sharples Our layered machine learning framework can enhance real-time transaction monitoring in high-value payment systems, which are a central piece of a country’s financial infrastructure. When tested on data from Canadian payment systems, it demonstrated potential for accurately identifying anomalous transactions. This framework could help improve cyber and operational resilience of payment systems. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C4, C45, C5, C55, D, D8, D83, E, E4, E42 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Money and payments, Payment and financial market infrastructures
Machine learning for economics research: when, what and how Staff analytical note 2023-16 Ajit Desai This article reviews selected papers that use machine learning for economics research and policy analysis. Our review highlights when machine learning is used in economics, the commonly preferred models and how those models are used. Content Type(s): Staff research, Staff analytical notes JEL Code(s): A, A1, A10, B, B2, B23, C, C4, C45, C5, C55 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Structural challenges, Digitalization and productivity
Predicting Changes in Canadian Housing Markets with Machine Learning Staff discussion paper 2023-21 Johan Brannlund, Helen Lao, Maureen MacIsaac, Jing Yang We apply two machine learning algorithms to forecast monthly growth of house prices and existing homes sales in Canada. Although the algorithms can sometimes outperform a linear model, the improvement in forecast accuracy is not always statistically significant. Content Type(s): Staff research, Staff discussion papers JEL Code(s): A, C, C4, C45, C5, C53, D, D2, R, R2, R3 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Real economy and forecasting