September 30, 2020

Volume X, Number 274

September 30, 2020

Subscribe to Latest Legal News and Analysis

September 29, 2020

Subscribe to Latest Legal News and Analysis

September 28, 2020

Subscribe to Latest Legal News and Analysis

RegTech: U.S. Regulator’s View on Artificial Intelligence in Risk Assessment

On 21 June at the OpRisk North America 2017 conference in New York, Scott W. Bauguess, Acting Director and Acting Chief Economist of the U.S. Securities and Exchange Commission’s (“SEC”) Division of Economic and Risk Analysis (“DERA”) gave a keynote speech on the use of artificial intelligence by regulators.  A transcript of the speech can be found here.  Bauguess provided some interesting background on the utility and use of big data and machine learning at the SEC to identify potential misconduct by market participants and investment managers, and the emerging use of artificial intelligence.

Bauguess’ speech discussed the SEC’s use of AI in its regulatory framework, initially discussing machine learning.  The SEC currently applies topic modeling methods, such as Latent Dilchlet Allocation (“LDA”).  LDA reviews text-based documents (e.g., registration disclosures) and reports on where, and to what extent, particular words appear in each document.  This occurs either by: analyzing the probability of words across documents, and within documents, to define the topics they represent (“unsupervised learning”); or incorporating human judgement and direction into the programming of the machine’s algorithms (“supervised learning”).

For investment managers, the SEC uses a two-stage approach to detect potential investment adviser misconduct.  In the first stage, the SEC uses “unsupervised” learning algorithms to identify unique behaviors.  Then it feeds the outputs of the first stage into a machine learning algorithm to predict the presence of risk for each investment manager.  Although this method has proven successful, it can produce false positives, and therefore SEC human staff still must review the outputs of these models.

Of particular note is need for ongoing human assessment of potential enforcement actions due to the inherent limitations of current technology.  However, it is obvious that machine learning algorithms and AI will be a critical tool for enforcement and regulation in the future.  However, Bauguess does think it reasonable that AI could develop to: aggregate data, assess whether securities laws have been violated, and generate detailed reports on market risk and potential enforcement actions.

Copyright 2020 K & L GatesNational Law Review, Volume VII, Number 180


About this Author

Todd Gibson, Investment Management Group, Attorney, KL Gates Law Firm

Mr. Gibson is a member of the firm’s Investment Management Group, and his practice focuses primarily on international aspects of investment management services and globally-distributed fund products. His clients include U.S. and non-U.S. investment managers, U.S. broker/dealers, hedge funds, and private equity funds, and he acts as special U.S. counsel to funds organized under the European UCITS directive. Mr. Gibson also acts as fund counsel to U.S. registered investment advisers and U.S. mutual funds registered under the Investment Company Act of 1940. He also...

Evan Glover, KL Gates Law Firm, Finance Attorney

Evan Glover is an associate in the firm’s Pittsburgh office, where he is a member of the Investment Management, Hedge Funds and Alternative Investments practice group. Mr. Glover also advises clients on litigation matters relating to disputes involving financial institutions and financial services.