The Role of Big Data, Machine Learning, and AI in Assessing Risks: A Regulatory Perspective

SEC Keynote Address: OpRisk North America 2017

6 Pages Posted: 18 Aug 2018

See all articles by Scott W. Bauguess

Scott W. Bauguess

University of Texas at Austin - Department of Finance

Date Written: June 21, 2017

Abstract

Artificial Intelligence, perhaps better known by its two-letter acronym “AI,” has been the fodder of science fiction writing for decades. But the technology underlying AI research has recently found applications in the financial sector – in a movement that falls under the banner of “Fintech.” And the same underlying technology (machine learning and AI) is fueling the spinoff field of “RegTech,” to make compliance and regulatory-related activities easier, faster, and more efficient. Like many financial institutions and other market participants, the Commission has made recent and rapid advancements with analytic programs that harness the power of big data (a.k.a "SupTech"). They are driving SEC surveillance programs and allowing innovations in many market risk assessment initiatives. My remarks are intended to highlight many of the promises – but also the limitations – of machine learning, big data, and AI in market regulation.

Keywords: AI, Machine Learning, FinTech, RegTech, SupTech, Market Regulation, Surveillance, SEC

JEL Classification: G10, G28, G00, G28

Suggested Citation

Bauguess, Scott W., The Role of Big Data, Machine Learning, and AI in Assessing Risks: A Regulatory Perspective (June 21, 2017). SEC Keynote Address: OpRisk North America 2017, Available at SSRN: https://ssrn.com/abstract=3226514 or http://dx.doi.org/10.2139/ssrn.3226514

Scott W. Bauguess (Contact Author)

University of Texas at Austin - Department of Finance ( email )

Red McCombs School of Business
Austin, TX 78712
United States

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