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Some financial regulatory implications of artificial intelligence

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  • Wall, Larry D.

Abstract

Artificial intelligence has been playing an increasingly large role in the economy and this trend seems likely to continue. This paper begins with a high-level overview of artificial intelligence, including some of its important strengths and weaknesses. It then discusses some of the ways that AI affect the evolution of the financial system and financial regulation.

Suggested Citation

  • Wall, Larry D., 2018. "Some financial regulatory implications of artificial intelligence," Journal of Economics and Business, Elsevier, vol. 100(C), pages 55-63.
    • Handle: RePEc:eee:jebusi:v:100:y:2018:i:c:p:55-63
    DOI: 10.1016/j.jeconbus.2018.05.003
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    References listed on IDEAS

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    Cited by:

    1. Rodrigues, Ana Rita D. & Ferreira, Fernando A.F. & Teixeira, Fernando J.C.S.N. & Zopounidis, Constantin, 2022. "Artificial intelligence, digital transformation and cybersecurity in the banking sector: A multi-stakeholder cognition-driven framework," Research in International Business and Finance, Elsevier, vol. 60(C).
    2. Mustafa Pamuk & Matthias Schumann, 2023. "Opening a New Era with Machine Learning in Financial Services? Forecasting Corporate Credit Ratings Based on Annual Financial Statements," IJFS, MDPI, vol. 11(3), pages 1-20, July.
    3. Chao, Xiangrui & Ran, Qin & Chen, Jia & Li, Tie & Qian, Qian & Ergu, Daji, 2022. "Regulatory technology (Reg-Tech) in financial stability supervision: Taxonomy, key methods, applications and future directions," International Review of Financial Analysis, Elsevier, vol. 80(C).
    4. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    5. Cosma, Simona & Rimo, Giuseppe & Torluccio, Giuseppe, 2023. "Knowledge mapping of model risk in banking," International Review of Financial Analysis, Elsevier, vol. 89(C).
    6. Larry D. Wall, 2020. "Is stricter regulation of incentive compensation the missing piece?," Journal of Banking Regulation, Palgrave Macmillan, vol. 21(1), pages 82-94, March.
    7. W. Scott Frame & Larry Wall & Lawrence J. White, 2018. "Technological Change and Financial Innovation in Banking: Some Implications for FinTech," Working Papers 18-28, New York University, Leonard N. Stern School of Business, Department of Economics.
    8. Céline Baud & Nathalie Lallemand-Stempak, 2024. "Quantitative technologies and reflexivity: The role of tools and their layouts in the case of credit risk management," Post-Print hal-04419872, HAL.
    9. 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.
    10. Henri Fraisse & Matthias Laporte, 2021. "Return on Investment on AI: The Case of Capital Requirement," Working papers 809, Banque de France.
    11. Fraisse, Henri & Laporte, Matthias, 2022. "Return on investment on artificial intelligence: The case of bank capital requirement," Journal of Banking & Finance, Elsevier, vol. 138(C).
    12. Massimo Preziuso & Franziska Koefer & Michel Ehrenhard, 2023. "Open banking and inclusive finance in the European Union: perspectives from the Dutch stakeholder ecosystem," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-27, December.
    13. Guangyu Mu & Yuhan Wang & Nan Gao & Xiurong Li, 2023. "A Novel Tripartite Evolutionary Game Model for Internet Consumer Financial Regulation," SAGE Open, , vol. 13(3), pages 21582440231, August.
    14. Oleksandr Melnychenko, 2020. "Is Artificial Intelligence Ready to Assess an Enterprise’s Financial Security?," JRFM, MDPI, vol. 13(9), pages 1-19, August.
    15. Jurgita Bruneckiene & Robertas Jucevicius & Ineta Zykiene & Jonas Rapsikevicius & Mantas Lukauskas, 2019. "Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
    16. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    17. Garg, Poonam & Gupta, Bhumika & Chauhan, Ajay Kumar & Sivarajah, Uthayasankar & Gupta, Shivam & Modgil, Sachin, 2021. "Measuring the perceived benefits of implementing blockchain technology in the banking sector," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    18. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.
    19. Subhadeep Mukhopadhyay, 2021. "InfoGram and Admissible Machine Learning," Papers 2108.07380, arXiv.org, revised Aug 2021.
    20. Baud, Céline & Lallemand-Stempak, Nathalie, 2024. "Quantitative technologies and reflexivity: The role of tools and their layouts in the case of credit risk management," Accounting, Organizations and Society, Elsevier, vol. 112(C).
    21. Dietlmeier, Simon Frederic, 2024. "Industrial Policy for Emerging Technologies: The Case of Narrow AI and the Manufacturing Value Chain as Blueprint for the Industrial Metaverse," MPRA Paper 121183, University Library of Munich, Germany.
    22. Thomas J. Chemmanur & Michael B. Imerman & Harshit Rajaiya & Qianqian Yu, 2020. "Recent Developments In The Fintech Industry," Journal of Financial Management, Markets and Institutions (JFMMI), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 1-31, June.
    23. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.

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