12/6/2019 - Task Force on Artificial Intelligence: Robots on Wall Street... - (EventID=110292)

Video Description

Friday, December 6, 2019 (9:30 AM) -- Hearing: Task Force on Artificial Intelligence: Robots on Wall Street: The Impact of AI on Capital Markets and Jobs in the Financial Services Industry Connect with the House Financial Services Committee Get the latest news: https://financialservices.house.gov/ Follow us on Facebook: https://www.facebook.com/FinancialDems/ Follow us on Twitter: https://twitter.com/FSCDems This single-panel hearing will have the following witnesses: • Dr. Charlton McIlwain, Vice Provost for Faculty Engagement and Development and Professor of Media, Culture, and Communication at NYU • Dr. Marcos Lopez de Prado, Professor of Practice, Engineering School, Cornell University and Chief Investment Officer, True Positive Technologies • Ms. Rebecca Fender, CFA, Senior Director, Future of Finance, Chartered Financial Analyst Institute • Ms. Kirsten Wegner, Chief Executive Officer, Modern Markets Initiative • Ms. Martina Rejsjö, Head of Nasdaq Market Surveillance, Nasdaq Stock Market Overview Technological innovation has long affected U.S. industries and workplaces, and the financial services industry is no exception. The asset management industry, for example, uses artificial intelligence (“AI”) to aggregate data, including public company financial reports, macroeconomic data, and asset prices in related markets, and performs analyses for the purpose of making decisions about where to invest the capital it holds. At the same time, the rapid development of AI and algorithmic decision-making has the potential to create computer programs that exceed human capabilities in certain manual tasks, such as quantitatively analyzing data sets and predicting outcomes based on those analyses, thus replacing the need for humans. When further examining AI’s impact on capital markets and jobs in the financial services industry, AI potentially increases workers productivity (and thereby their labor market value) for those who perform complex, non-routine tasks. Some examples where AI helps workers perform better include: (1) reducing the time it takes to process data and organize information; (2) streamlining regulatory processes; and, (3) increasing the precision of work performed. Productivity improvements also can translate into increased labor demand if consumer demand responds sufficiently to the lower costs, better quality, or increased variety in the financial services products. Furthermore, there are concerns regarding how many jobs may be at risk due to new automation technologies in the coming years – one study estimated 30 percent of finance and insurance jobs in developed economies are at risk of automation by 2029. Meanwhile, automation technologies can also create new employment areas, potentially creating opportunity for workers displaced from traditional financial sector jobs or newly-entering the sector. For example, in recent years firms like JP Morgan Chase have reportedly hired more software developers than Google or Microsoft. Below is background on how AI and automation affects investment decisions, its impact on the workforce in the financial services sector, and how AI may affect oversight, risk management and regulatory compliance by financial firms. Investment Decisions Investment fund managers use AI along with large data sets to enhance their investment strategies and understanding of the earnings prospects of an asset, affecting how these managers price assets (e.g., stocks, bonds, and derivatives). Hedge funds and high-frequency traders were the earliest adopters of AI decision-making within the financial services industry. Some specific applications include: (1) “robo-advisers” (2) trading and portfolio management and the data analysis and execution of trades, and (3) supervisory functions. Further, AI utilization is hoped to overcome certain irrational and emotional tendencies of human decision making, including: loss aversion (feeling greater negative emotion over losses relative to positive emotion over equivalent gains), trend-chasing (being influenced by the investment activities of others), and anchoring (being unwilling to consider new information that contradicts initial assessments). However, AI has its own biases and deficiencies that may limit its potential to replace human workers in the investment sector. One key concern is the auditability and accountability of the machine decisions. In the context of deep learning, AI could make investment decisions on its own. Human attempts to audit, replicate, or rationalize such decisions may not succeed. As such, some question the assignment of the responsibilities for AI decisions, when things go wrong. In a recent case concerning a $20 million AI-related investment loss, a Stanford University law professor commented, “people tend to assume that algorithms are faster and better decision-makers than human traders.... Hearing page: https://financialservices.house.gov/calendar/eventsingle.aspx?EventID=404858