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Comparing minds and machines: implications for financial stability

Author

Listed:
  • Marcus Buckmann
  • Andy Haldane
  • Anne-Caroline Hüser

Abstract

Is human or artificial intelligence more conducive to a stable financial system? To answer this question, we compare human and artificial intelligence with respect to several facets of their decision-making behaviour. On that basis, we characterize possibilities and challenges in designing partnerships that combine the strengths of both minds and machines. Leveraging on those insights, we explain how the differences in human and artificial intelligence have driven the usage of new techniques in financial markets, regulation, supervision, and policy-making, and discuss their potential impact on financial stability. Finally, we describe how effective mind–machine partnerships might be able to reduce systemic risks.

Suggested Citation

  • Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021. "Comparing minds and machines: implications for financial stability," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 479-508.
    • Handle: RePEc:oup:oxford:v:37:y:2021:i:3:p:479-508.
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    File URL: http://hdl.handle.net/10.1093/oxrep/grab017
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    Cited by:

    1. Shidi Deng & Maximilian Schiffer & Martin Bichler, 2025. "Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning," Papers 2503.11270, arXiv.org.
    2. Nana Chai & Mohammad Zoynul Abedin & Xiaoling Wang & Baofeng Shi, 2025. "Growth potential of machine learning in credit risk predicting of farmers in the industry 4.0 era," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(3), pages 2163-2185, July.
    3. Dr Djefedie Stéphane Contard, 2025. "Risks Associated with Innovation: The Case of Artificial Intelligence," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(10), pages 5085-5097, October.
    4. Karolis Liaudinskas, 2022. "Human vs. Machine: Disposition Effect among Algorithmic and Human Day Traders," Working Paper 2022/6, Norges Bank.
    5. Chmielewska Anna & Sławiński Andrzej, 2021. "Climate crisis, central banks and the IMF reform," Economics and Business Review, Sciendo, vol. 7(4), pages 7-27, December.
    6. Zhao, Xin & Zhai, Guoqing & Charles, Vincent & Gherman, Tatiana & Lee, Hyoungsuk & Pan, Tuan & Shang, Yuping, 2024. "Enhancing enterprise investment efficiency through artificial intelligence: The role of accounting information transparency," Socio-Economic Planning Sciences, Elsevier, vol. 96(C).
    7. Epivent, Andréa & Lambin, Xavier, 2024. "On algorithmic collusion and reward–punishment schemes," Economics Letters, Elsevier, vol. 237(C).

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    Keywords

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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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