Departmental Papers

Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance

By El Bachir Boukherouaa, Ghiath Shabsigh, Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S Iskender, Alin T Mirestean, Rangachary Ravikumar

October 22, 2021

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El Bachir Boukherouaa, Ghiath Shabsigh, Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S Iskender, Alin T Mirestean, and Rangachary Ravikumar. Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance, (USA: International Monetary Fund, 2021) accessed December 18, 2024

Disclaimer: The views expressed herein are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Subject: Artificial intelligence, Cyber risk, Economic sectors, Financial sector, Financial sector policy and analysis, Financial sector stability, Financial services, Machine learning, Technology

Keywords: Artificial intelligence, Artificial Intelligence, Cyber risk, Cybersecurity, Data Privacy, Embedded Bias, Financial Regulation, Financial sector, Financial sector stability, Financial Stability, Global, Machine learning, Machine Learning, Machine learning algorithm, ML deployment, ML in finance, ML system, Risk Management, Strategy landscape

Publication Details

  • Pages:

    34

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Departmental Paper No 2021/024

  • Stock No:

    PDEORAIFEA

  • ISBN:

    9781589063952

  • ISSN:

    2616-5333

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