Financial industry compliance with Big Data and analytics

Christina Mcglosson and Marco Enriquez
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Christina Mcglosson: Associate Director and Counsellor in the Division of Enforcement, Commodity Futures Trading Commission, USA
Marco Enriquez: Applied Mathematician in the Division of Economic and Risk Analysis, US Securities and Exchange Commission

Journal of Financial Compliance, 2019, vol. 3, issue 2, 103-117

Abstract: The use of Big Data, machine learning and artificial intelligence (AI) is rapidly reshaping the financial regulatory landscape and changing ways in which financial institutions manage risks and ensure regulatory compliance. On one side, regulators have begun to use advanced analytical tools designed to identify fraud and misconduct, predict market participants’ behaviour and detect aberrational patterns in the markets and registrant disclosures. On the other side, financial institutions are increasingly relying on RegTech to help mitigate risks and ensure compliance with relevant securities and commodities rules and regulations. As artificial intelligence programmes become more sophisticated and less predictable, new questions arise regarding the applicability of the current legal framework and liability theories to new technologies. This paper describes how financial firms and regulatory agencies are integrating artificial intelligence systems into their compliance and surveillance programmes, respectively. It then examines several legal theories that may hold firms or individuals liable for violations committed by advanced automated systems.

Keywords: Big Data; machine learning; artificial intelligence; Securities and Exchange Commission (SEC); Commodity Futures Trading Commission (CFTC); regulatory compliance; automated trading systems; market manipulation; black-box algorithms; agency law; CFTC Whistleblower Office (search for similar items in EconPapers)
JEL-codes: E5 G2 K2 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:aza:jfc000:y:2019:v:3:i:2:p:103-117

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