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Banking on AI: mandating a proactive approach to AI regulation in the financial sector
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Despite an emerging international consensus on principles of AI governance, lawmakers have so far failed to translate those principles into regulations in the financial sector. Perhaps, in order to remain competitive in the global race for AI supremacy without being typecast as stifling innovation, typically cautious financial regulators are unusually allowing the introduction of experimental AI technology into the financial sector, with few controls on the unprecedented risks to consumers and financial stability. Once an unregulated AI software causes serious economic harm, a public and regulatory backlash would lead to over-regulation that could harm innovation of this potentially beneficial technology. Artificial intelligence is rapidly influencing the financial sector with innumerable potential benefits, such as enhancing financial services and improving regulatory compliance. This article argues that the best way to encourage a sustainable future in AI innovation in the financial sector is to support a proactive regulatory approach prior to any financial harm occurring. This proactive approach should implement rational regulations that embody jurisdiction-specific rules in line with carefully construed international principles.
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“De acuerdo con Truby et al. (2020), existe un consenso internacional sobre los principios de la gobernanza de la IA, pero los legisladores no han logrado traducir esos principios en regulaciones para el sector financiero.”
From Hacia una normativa sobre la inteligencia artificial (IA): consideraciones clave y regulaciones internacionales (2024) · cites this paper as Truby et al. (2020)
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- See also, DA Zetzsche, DW Arner, RP Buckley, and B Tang, Artificial Intelligence in Finance: Putting the Human in the Loop (Feb- ruary 2020). CFTE Academic Paper Series: Centre for Finance, Technology and Entrepreneurship, no. 1. Available at SSRN: https://ssrn.com/abstract= at p.17.
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Metadata
- Authors
- Truby, J., Brown, R., & Dahdal, A.
- Affiliations
- Qatar University (QA)
- Venue
- Law and Financial Markets Review
- Publisher
- Informa UK Limited
- Published
- 2020-04-02
- Volume
- 14
- Issue
- 2
- Pages
- 110-120
- ISSN
- 1752-1440 (ISSN-L)
- 1752-1459
- OpenAlex
- W3024628422
- Cited by
- 175
- References
- 0
- Citations by year
- 2026: 18 · 2025: 52 · 2024: 50 · 2023: 32 · 2022: 14 · 2021: 7 · 2020: 2
- FWCI
- 89.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Law and Financial Markets Review (publishedVersion, cc-by)
- SSRN Electronic Journal (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Funders
- Qatar National Research Fund
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Business, Management and Accounting › Management Information Systems › FinTech, Crowdfunding, Digital Finance
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- AI regulation, financial services, proactive regulation, financial stability, consumer protection, innovation and regulation, international regulatory cooperation, financial regulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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