Transcripts For CSPAN Hearing On Impact Of Artificial Intell

S an hour and a half. The task force will now come to order. Without objection, the chairs authorized to declare a recess at any time without objection members of the full committee are authorized to participate in todays hearing consistent with the committees practice. This hearing is entitled robots on wall street, the impact of ai on jobs in the Financial Services industry. The chair will recognize himself for five minutes for an opening statement. Thank you all for joining us today for what should be a having interesting hearing of this task force. Were looking at exploring how ai is being deployed in Capital Markets from automated trading to Investment Management decisions. An e minutes for opening statement. First off, thank you for joining us today for what should be a thisinteresting hearing of task force. Today were looking at exploring how a. I. Is being deployed in Capital Markets from automated Portfolio Allocation to Investment Management decisions. Were also going to consider how the use of this technology is changing the nature of work in rendering ervices, some jobs obsolete and changing the skill sets needed to excel in others. It would not be much of an exaggeration today to say that quite literally is run by computers. Of those are the days screaming on the new york stock pour ge and they would over the ticker statement and Financial Statements to gleam value. Into a companys i actually hear about those days from the limo driver who takes e back who used to be a floor trader on the merc. Today, floor trades are orders are d executed in microseconds. E. T. F. s rely on lgogh rhythmic models to make sure its properly waved to whatever benchmark is tracking. Quan tative funds scour all sorts of market data to find the price that have the most momentum or highest dividends or look for correlations that and in in the market external data feeds to provide the most value for investors. Notable nk its very lot of the shakeup were seeing in those markets is really a reflection of sort of take all nature of digital economies. Hat any Digital Business, purely Digital Business is a more monopoly. As it becomes the distinguished advertised, you will see rewards go to a smaller number of dominant players. Emphasize its not evil. Its simply a natural reflection digital ture of the marketplace. Other Asset Managers may use complex s to perform research and analysis in real time on big data sets. Scouring social media sites, web traffic, online transactions, and just about of. Hing else you can think this is, i guess, good in terms of having the market reflect all known data, but there are abuse of corners. Imagine what it would be ert if you had a 10second early look at trumps feed how much money you could make trading off that, for example. Computer types of managed tuck fundz make up about approximately 31 market. Public equities hedge funds, other mutual funds, manage just 20 of the market. Socalled the computerization of our stock markets has a number of benefits. Trades has executing gone way down. Sometimes to zero dollars. In theres more liquidity the market. Passive funds charge less than of assets while active managers often charge 20 times that much. It creates additional questions, however. As in the 2010 flash crash and the more recent mini flash shown algogh hythmic trading can provide unpredictable consequences and between e information different types of investors. As firms with more and faster to data sets can obtain a competitive advantage. Another broader question is how impactinglopments are the nature of jobs in the Financial Services industry. Wells fargo Research Report estimated that technological efficiencies would 200,000 job cuts over the next decade in the u. S. Banking industry. Hile these cuts will certainly affect back office, call center, and Customer Service positions, widespread. L be many Front Office Workers such s bankers, traders, financial analyses could also see their head count drop by almost a hird, according to a report released earlier this year. The report also found that 40 of existing jobs at financial automated with current technology. To first order, if you spend day staring at a big screen and in particular if you your e a large paycheck, job will be at risk. Understanding the skills that will be needed to excel in the Financial Services industry of and how we can encourage these skills is one of the issues that we must tackle tackle early. In a world where many functions can be done by automated a. I. Does that t role leave for humans . So i very much look forward to hearing from our witnesses on issues. With that id like to recognize the task force Ranking Member, y friend from georgia, mr. Loudermilk, for five minutes. Mr. Loudermilk thank you, mr. Chairman. Want to thank each of our witnesses here today. Thank you for taking time to be here to discuss this issue while is fixated america on other things going on here. Something that may not resonate on the major networks, veryt is something that is important, has an impact on our lives, positively but also negatively. Its important we be looking into this. Task you know, today the force will examine the intersection between technology and the Capital Markets. Years, there have been many technological developments, including the adoption of and icial intelligence automation that have redefined nd reshaped trading and investing. The first is on the new York City Stock Exchange were made in manual 1700s using a paper intensive process. For many years, buyers and communicated about numbers over the phone. Today, trading and investing are one on digital platforms and investors can trade securities virtually anywhere in the world technology. It has benefited the markets in many ways. For investorstive by leading to lower overhead and transaction costs which to record investment returns over the last decade. Several major Asset Management offer 0 commissions which means investors can buy and sell stocks essentially for capture more of the growth of their investments. This would not be possible trading. Lectronic Digital Trading platforms also provide investors with access to lowcost reference and advice roboadvisors. Electronic trading makes markets more efficient by allowing prices, arches for better processing of large sets of data and price information. Of technology on can also lower firms barriers more ry, foster competition, improve risk management, and increase Market Access for investors. Addition to these core benefits, there are many other cases of Companies Using a. I. To improve efficiencies in the Capital Markets in unique ways. Or example, some Clearing Companies are using a. I. To optimize the settlement of cybersecurityance and fraud detection. Some selfregulatory using ations are also a. I. In reg tech and market surveillance. While there are many benefits to electronic trading, it can challenges. One challenge which is at the forefront of our discussion is the disruption of job market. Rise of automated trading has displaced many floor traders, Job Opportunities in fields like code writing, cloud telecommunications, fiberoptics and Data Analysis are growing. Here is some concern that High Frequency trading can contribute to volatility, but new evidence uggests that high frequent trading does not increase volatility and can actually improve liquidity. Is also some concern that firms dont have the latest firms that dont have the latest technology could be competed out of the markets. In mind rtant to keep that not all types of electronic trading are the same and i look forward to learning more from witnesses about the differences between automated rading, algogh rhythmic fraying algorithmic trading. And also looking at the issues in this space. Of source protection code. Because algorithms are a core intellectual property, they must be protected. Passed a out of we bill out of this committee and the house on a bipartisan basis the congress to ensure that securities and Exchange Commission issues a subpoena before obtaining these rather than getting through routine exams. We can work, i hope together on a bill this ng co. Back. K you and i yield mr. Foster thank you. Today were welcoming the provost and professor of media, culture and n. Y. U. Cation at deprado, cornell university, chief investment fficer of true positive technologies. Ms. Rebecca fender, c. F. A. , of or director, future finance, chartered financial analyst institute. Mrs. Wagner, chief executive officer modern markets initiative. Racial, head of nasdaq surveillance, nasdaq stock market. Our oral testimony will be limited to five minutes and without objection, your full written statement will be made part of the record. Doctor, you are now recognized for five minutes to give an oral presentation of your testimony. Foster and Ranking Member loudermilk, thank you for inviting me to testify. While my written remarks cover four key areas, my oral remarks two the implications of automation on the workforce mitigating algorithmic bias. We are concerned in the Financial Services sector. First, the Financial Services ripe for automation and algorithm. The in tech sector is on rise. Third large number of workers will be displaced in the Financial Services sector even automation and a. I. Development is protected to create new types of jobs. Cilwain if all of this is true, then the cause for concern for clear. Fact that h the africanamericans and latin workers, in particular, are underrepresented in the Financial Services sector workforce. Africanamericans, hispanics, and asians make up only 22 of he Financial Service industry workforce. Africanamerican representation in the Financial Services sector senior entry level and level jobs declined from 2007 to 2015. Less than 3. 5 of all Financial Planners in the u. S. Are black latin. Africanamericans make up just 2. 9 of hispanics just the securities subsector. Asians make just make up just 2. 8 of the central banking and insurance subsectors. My point is simple. Groups that are extremely underrepresented in the Services Industry will be most at risk in terms of automation and the escalation of fin tech development. This is especially true given vast underrepresentation of africanamericans and latins in the workforce. If we are to mitigate the likelihood that automation will disproportionately and affect those already underrepresented in the we ncial Services Industry, must plan ahead long into the future rather than allowing the market to run its course towards predictable outcomes. Deterring subject of algorithmic mic bias, we need to deploy these more s and provide oversight from industry, overnment and nongovernmental bodies to see what outcome they produce. Make algorithms more mitigate such potential bias rather than their effects ct once their damage is done. But i want to emphasize that specially when it comes to mitigating the potential outcome rhythms have on communities of algorithms of color, munities technologists is not a complete solution. Drawing on a by former rights leader who had a understanding of computerized automation as they existed in his time. The unskilled and semi skilled worker is the victim but cybernation invades stronghold of the american middle class as once proud workers began sinking into the alienated underclass. Meet the new r poor, we know that automation is the curse but not the only curse. Problem is not automation but social injustice itself. Financial example the findings from a recent National Research economic study entitled discrimination in area. N tech their researchers taught to an \gogh rhythm system could hurt. The findings were mix. Yes, the system discriminated less than the traditional process but also meant that the process discriminated against a number of africanamericans and latin loan applicants. Even though the system did not balance discriminate in terms of loan approval, it did latin inate black and users in terms of price. One of the key conclusions of fin tudy states that both tech and facetoface lenders may discriminate and mortgage through pricing strategies. We are just scratching the surface and the role of pricing the egy discrimination in use. Ithmic area of data ending may discriminate less. But it does not eliminate it. Ven with the aid of a fair, accurate, and transparent ystem, Racial Discrimination persists. Thank you, again, allowing me the opportunity to contribute to these proceedings. You. Oster thank mr. De prado. De prado thank you for to be here today. Oreign tasks until recently, only expert humans could accomplish. One is management of investments. For two reasons. First, one of the most successful things in history to be algorithmic. He key is their decisions are objective, can be improved over time. Automation enables cost reductions. Including order forecasting, other things. Number l a. I. Creates a of challenges for those involved in the financial and insurance industry. Many of whom will lose their jobs, not because they will be replaced by machines, but because they have not been alongside work algorithms. The returning of these workers important and difficult task. Not everything is bad news. Skills become more the genders, etween ethnicities and other classifications should narrow. A great equalizer. Returning our existing workforce importance. Al however, its not enough. We must make sure that the that american universities have contributed and developed remains in our country. Founders of the next google, very , or apple are this morning attending a math or ngineering class at one of our universities. These future entrepreneurs are in our country on a visa. Unless we help them, they ill return to the countries of origin. And they will compete against us. On a different note, id like to attention to two practical examples of the learning n of machine algorithms to regulatory oversight. Crowdsourcing of investigations. One of the most challenging to s faced by regulators is identify market manipulators data. This is a challenging task. Like searching for a needle in a haystack. The practical approach is for help ofrs to enroll the those. Regulators could minimize transaction data and offer to the Worldwide Community scientists who will be rewarded with a portion of the finds. Time that Financial Markets Something Like the couldcrash, this approach lead to faster identification of potential market manipulators. Second embodiment of reg tech, they are filled with false a Financial Firms even online tools and large hedge funds fall constantly for this trap, leading to investor losses. To require is Financial Firms to record all those involved in the development of the product. This information, say its could overfit and this probability reported. Finally, i would like to conclude my remarks with the bias. Sion of yes, Machine Learning algorithms can incorporate human biases. News is we have a in er chance in protecting algorithms. The reason is that we can algorithms to a batch of andomized controlled experiments and have them perform as intended. Can assist human ecisionmakers that humans can override. Hus and as algorithmic investi investing goes on, many can reap the benefits of this technology. Thank you for allowing me to contribute to this hearing and i look forward to your questions. Mr. Foster thank you. Ms. Bender, youre now recognized for five minutes to presentation of your testimony. Foster, r chairman loudermilk, and members of the task force, thank you for inviting me to testify here today. My name is rebecca fender and i senior director of the future of finance at c. F. A. Is our thoughtch leadership platform. C. F. A. Institute is the largest association of investment professionals in the world with 170,000 c. F. H. Countries. Ders in 76. F. H. Is best known for the Charter Financial analyses which graduate level exam. They must have four years of experience. C. F. A. Institute is a nonpartisan institution and voice on e a leading the globalization of market protection. C. F. A. Published a paper on the of the future, examining the changing roles in five to 10 years. Among the c. F. A. Institute members and candidates we 43 think the role they perform today will be substantially different in five to 10 years time. Was greater than 50 among financial advisors, traders, analyses. Another 5 do not think their role will exist by then. One of the catalyst is technology. Its a pyramid. T the foundation we have basic applications. Everythi everyone will need to learn to and some differently eople will face tech subis i constitution. Substitution. Technology will enhance work. Are e top there hyperpartisan roles. Believes the te key is ongoing learning. Curriculum now has Machine Learning. And among those surveyed in the interestport, 58 have in Data Analysis coding r. Guages like python and similarly, Data Visualization and interpretation are areas expressed han half from in. In terms of the role of Financial Services<\/a> industry. The chair will recognize himself for five minutes for an opening statement. Thank you all for joining us today for what should be a having interesting hearing of this task force. Were looking at exploring how ai is being deployed in Capital Markets<\/a> from automated trading to Investment Management<\/a> decisions. An e minutes for opening statement. First off, thank you for joining us today for what should be a thisinteresting hearing of task force. Today were looking at exploring how a. I. Is being deployed in Capital Markets<\/a> from automated Portfolio Allocation<\/a> to Investment Management<\/a> decisions. Were also going to consider how the use of this technology is changing the nature of work in rendering ervices, some jobs obsolete and changing the skill sets needed to excel in others. It would not be much of an exaggeration today to say that quite literally is run by computers. Of those are the days screaming on the new york stock pour ge and they would over the ticker statement and Financial Statements<\/a> to gleam value. Into a companys i actually hear about those days from the limo driver who takes e back who used to be a floor trader on the merc. Today, floor trades are orders are d executed in microseconds. E. T. F. s rely on lgogh rhythmic models to make sure its properly waved to whatever benchmark is tracking. Quan tative funds scour all sorts of market data to find the price that have the most momentum or highest dividends or look for correlations that and in in the market external data feeds to provide the most value for investors. Notable nk its very lot of the shakeup were seeing in those markets is really a reflection of sort of take all nature of digital economies. Hat any Digital Business<\/a>, purely Digital Business<\/a> is a more monopoly. As it becomes the distinguished advertised, you will see rewards go to a smaller number of dominant players. Emphasize its not evil. Its simply a natural reflection digital ture of the marketplace. Other Asset Managers<\/a> may use complex s to perform research and analysis in real time on big data sets. Scouring social media sites, web traffic, online transactions, and just about of. Hing else you can think this is, i guess, good in terms of having the market reflect all known data, but there are abuse of corners. Imagine what it would be ert if you had a 10second early look at trumps feed how much money you could make trading off that, for example. Computer types of managed tuck fundz make up about approximately 31 market. Public equities hedge funds, other mutual funds, manage just 20 of the market. Socalled the computerization of our stock markets has a number of benefits. Trades has executing gone way down. Sometimes to zero dollars. In theres more liquidity the market. Passive funds charge less than of assets while active managers often charge 20 times that much. It creates additional questions, however. As in the 2010 flash crash and the more recent mini flash shown algogh hythmic trading can provide unpredictable consequences and between e information different types of investors. As firms with more and faster to data sets can obtain a competitive advantage. Another broader question is how impactinglopments are the nature of jobs in the Financial Services<\/a> industry. Wells fargo Research Report<\/a> estimated that technological efficiencies would 200,000 job cuts over the next decade in the u. S. Banking industry. Hile these cuts will certainly affect back office, call center, and Customer Service<\/a> positions, widespread. L be many Front Office Workers<\/a> such s bankers, traders, financial analyses could also see their head count drop by almost a hird, according to a report released earlier this year. The report also found that 40 of existing jobs at financial automated with current technology. To first order, if you spend day staring at a big screen and in particular if you your e a large paycheck, job will be at risk. Understanding the skills that will be needed to excel in the Financial Services<\/a> industry of and how we can encourage these skills is one of the issues that we must tackle tackle early. In a world where many functions can be done by automated a. I. Does that t role leave for humans . So i very much look forward to hearing from our witnesses on issues. With that id like to recognize the task force Ranking Member<\/a>, y friend from georgia, mr. Loudermilk, for five minutes. Mr. Loudermilk thank you, mr. Chairman. Want to thank each of our witnesses here today. Thank you for taking time to be here to discuss this issue while is fixated america on other things going on here. Something that may not resonate on the major networks, veryt is something that is important, has an impact on our lives, positively but also negatively. Its important we be looking into this. Task you know, today the force will examine the intersection between technology and the Capital Markets<\/a>. Years, there have been many technological developments, including the adoption of and icial intelligence automation that have redefined nd reshaped trading and investing. The first is on the new York City Stock Exchange<\/a> were made in manual 1700s using a paper intensive process. For many years, buyers and communicated about numbers over the phone. Today, trading and investing are one on digital platforms and investors can trade securities virtually anywhere in the world technology. It has benefited the markets in many ways. For investorstive by leading to lower overhead and transaction costs which to record investment returns over the last decade. Several major Asset Management<\/a> offer 0 commissions which means investors can buy and sell stocks essentially for capture more of the growth of their investments. This would not be possible trading. Lectronic Digital Trading<\/a> platforms also provide investors with access to lowcost reference and advice roboadvisors. Electronic trading makes markets more efficient by allowing prices, arches for better processing of large sets of data and price information. Of technology on can also lower firms barriers more ry, foster competition, improve risk management, and increase Market Access<\/a> for investors. Addition to these core benefits, there are many other cases of Companies Using<\/a> a. I. To improve efficiencies in the Capital Markets<\/a> in unique ways. Or example, some Clearing Companies<\/a> are using a. I. To optimize the settlement of cybersecurityance and fraud detection. Some selfregulatory using ations are also a. I. In reg tech and market surveillance. While there are many benefits to electronic trading, it can challenges. One challenge which is at the forefront of our discussion is the disruption of job market. Rise of automated trading has displaced many floor traders, Job Opportunities<\/a> in fields like code writing, cloud telecommunications, fiberoptics and Data Analysis<\/a> are growing. Here is some concern that High Frequency<\/a> trading can contribute to volatility, but new evidence uggests that high frequent trading does not increase volatility and can actually improve liquidity. Is also some concern that firms dont have the latest firms that dont have the latest technology could be competed out of the markets. In mind rtant to keep that not all types of electronic trading are the same and i look forward to learning more from witnesses about the differences between automated rading, algogh rhythmic fraying algorithmic trading. And also looking at the issues in this space. Of source protection code. Because algorithms are a core intellectual property, they must be protected. Passed a out of we bill out of this committee and the house on a bipartisan basis the congress to ensure that securities and Exchange Commission<\/a> issues a subpoena before obtaining these rather than getting through routine exams. We can work, i hope together on a bill this ng co. Back. K you and i yield mr. Foster thank you. Today were welcoming the provost and professor of media, culture and n. Y. U. Cation at deprado, cornell university, chief investment fficer of true positive technologies. Ms. Rebecca fender, c. F. A. , of or director, future finance, chartered financial analyst institute. Mrs. Wagner, chief executive officer modern markets initiative. Racial, head of nasdaq surveillance, nasdaq stock market. Our oral testimony will be limited to five minutes and without objection, your full written statement will be made part of the record. Doctor, you are now recognized for five minutes to give an oral presentation of your testimony. Foster and Ranking Member<\/a> loudermilk, thank you for inviting me to testify. While my written remarks cover four key areas, my oral remarks two the implications of automation on the workforce mitigating algorithmic bias. We are concerned in the Financial Services<\/a> sector. First, the Financial Services<\/a> ripe for automation and algorithm. The in tech sector is on rise. Third large number of workers will be displaced in the Financial Services<\/a> sector even automation and a. I. Development is protected to create new types of jobs. Cilwain if all of this is true, then the cause for concern for clear. Fact that h the africanamericans and latin workers, in particular, are underrepresented in the Financial Services<\/a> sector workforce. Africanamericans, hispanics, and asians make up only 22 of he Financial Service<\/a> industry workforce. Africanamerican representation in the Financial Services<\/a> sector senior entry level and level jobs declined from 2007 to 2015. Less than 3. 5 of all Financial Planners<\/a> in the u. S. Are black latin. Africanamericans make up just 2. 9 of hispanics just the securities subsector. Asians make just make up just 2. 8 of the central banking and insurance subsectors. My point is simple. Groups that are extremely underrepresented in the Services Industry<\/a> will be most at risk in terms of automation and the escalation of fin tech development. This is especially true given vast underrepresentation of africanamericans and latins in the workforce. If we are to mitigate the likelihood that automation will disproportionately and affect those already underrepresented in the we ncial Services Industry<\/a>, must plan ahead long into the future rather than allowing the market to run its course towards predictable outcomes. Deterring subject of algorithmic mic bias, we need to deploy these more s and provide oversight from industry, overnment and nongovernmental bodies to see what outcome they produce. Make algorithms more mitigate such potential bias rather than their effects ct once their damage is done. But i want to emphasize that specially when it comes to mitigating the potential outcome rhythms have on communities of algorithms of color, munities technologists is not a complete solution. Drawing on a by former rights leader who had a understanding of computerized automation as they existed in his time. The unskilled and semi skilled worker is the victim but cybernation invades stronghold of the american middle class as once proud workers began sinking into the alienated underclass. Meet the new r poor, we know that automation is the curse but not the only curse. Problem is not automation but social injustice itself. Financial example the findings from a recent National Research<\/a> economic study entitled discrimination in area. N tech their researchers taught to an \\gogh rhythm system could hurt. The findings were mix. Yes, the system discriminated less than the traditional process but also meant that the process discriminated against a number of africanamericans and latin loan applicants. Even though the system did not balance discriminate in terms of loan approval, it did latin inate black and users in terms of price. One of the key conclusions of fin tudy states that both tech and facetoface lenders may discriminate and mortgage through pricing strategies. We are just scratching the surface and the role of pricing the egy discrimination in use. Ithmic area of data ending may discriminate less. But it does not eliminate it. Ven with the aid of a fair, accurate, and transparent ystem, Racial Discrimination<\/a> persists. Thank you, again, allowing me the opportunity to contribute to these proceedings. You. Oster thank mr. De prado. De prado thank you for to be here today. Oreign tasks until recently, only expert humans could accomplish. One is management of investments. For two reasons. First, one of the most successful things in history to be algorithmic. He key is their decisions are objective, can be improved over time. Automation enables cost reductions. Including order forecasting, other things. Number l a. I. Creates a of challenges for those involved in the financial and insurance industry. Many of whom will lose their jobs, not because they will be replaced by machines, but because they have not been alongside work algorithms. The returning of these workers important and difficult task. Not everything is bad news. Skills become more the genders, etween ethnicities and other classifications should narrow. A great equalizer. Returning our existing workforce importance. Al however, its not enough. We must make sure that the that american universities have contributed and developed remains in our country. Founders of the next google, very , or apple are this morning attending a math or ngineering class at one of our universities. These future entrepreneurs are in our country on a visa. Unless we help them, they ill return to the countries of origin. And they will compete against us. On a different note, id like to attention to two practical examples of the learning n of machine algorithms to regulatory oversight. Crowdsourcing of investigations. One of the most challenging to s faced by regulators is identify market manipulators data. This is a challenging task. Like searching for a needle in a haystack. The practical approach is for help ofrs to enroll the those. Regulators could minimize transaction data and offer to the Worldwide Community<\/a> scientists who will be rewarded with a portion of the finds. Time that Financial Markets<\/a> Something Like<\/a> the couldcrash, this approach lead to faster identification of potential market manipulators. Second embodiment of reg tech, they are filled with false a Financial Firms<\/a> even online tools and large hedge funds fall constantly for this trap, leading to investor losses. To require is Financial Firms<\/a> to record all those involved in the development of the product. This information, say its could overfit and this probability reported. Finally, i would like to conclude my remarks with the bias. Sion of yes, Machine Learning<\/a> algorithms can incorporate human biases. News is we have a in er chance in protecting algorithms. The reason is that we can algorithms to a batch of andomized controlled experiments and have them perform as intended. Can assist human ecisionmakers that humans can override. Hus and as algorithmic investi investing goes on, many can reap the benefits of this technology. Thank you for allowing me to contribute to this hearing and i look forward to your questions. Mr. Foster thank you. Ms. Bender, youre now recognized for five minutes to presentation of your testimony. Foster, r chairman loudermilk, and members of the task force, thank you for inviting me to testify here today. My name is rebecca fender and i senior director of the future of finance at c. F. A. Is our thoughtch leadership platform. C. F. A. Institute is the largest association of investment professionals in the world with 170,000 c. F. H. Countries. Ders in 76. F. H. Is best known for the Charter Financial<\/a> analyses which graduate level exam. They must have four years of experience. C. F. A. Institute is a nonpartisan institution and voice on e a leading the globalization of market protection. C. F. A. Published a paper on the of the future, examining the changing roles in five to 10 years. Among the c. F. A. Institute members and candidates we 43 think the role they perform today will be substantially different in five to 10 years time. Was greater than 50 among financial advisors, traders, analyses. Another 5 do not think their role will exist by then. One of the catalyst is technology. Its a pyramid. T the foundation we have basic applications. Everythi everyone will need to learn to and some differently eople will face tech subis i constitution. Substitution. Technology will enhance work. Are e top there hyperpartisan roles. Believes the te key is ongoing learning. Curriculum now has Machine Learning<\/a>. And among those surveyed in the interestport, 58 have in Data Analysis<\/a> coding r. Guages like python and similarly, Data Visualization<\/a> and interpretation are areas expressed han half from in. In terms of the role of Artificial Intelligence<\/a> in the nvestment industry, the organizing principle we see is Artificial Intelligence<\/a> plus intelligence. Or a. I. Plus h. I. In these middle and top levels hierarchy, y Investment Management<\/a> justice and Technology Teams<\/a> Work Together<\/a>. Augment niques can human intelligence to free and tment professionals enable smarter decisionmaking. Markets ensure how work. It unlocks unstructured data and identify patterns more efficiently than humans. I. Can amplify an investment teams performance but cannot replicate its creativity. A. I. Recent paper, pioneers in investment anagement justice, we identified three types of a. I. And big data applications that are emerging in Investment Management<\/a>. Are, first, the use of natural language processing, computer vision, and voice to efficiently process text and audio data. Machine learning techniques to improve the usedtiveness of algorithms in investment processes. And to process big data, alternative and you structured data for investment insights. Find that 10 of professionals are currently sing a. I. And Machine Learning<\/a> techniques in their investment processes. However, here are a few examples study of what the a. I. Pioneers are doing. Sachs research industry, construction they use Geospatial Data<\/a> by youries. Second quarries. Econd, they study psychology textbooks to study patterns of eception in children and criminals. Hey will determine where omission, obfuscation and blame are being used. A product as had available since 2009 which analyzes the potential effect of on valuations. They process two million ocuments a day through their Machine Learning<\/a> platform. This was alternative data used by hedge funds at first but now of their clients use it. Just as the investment industry technology, greater they can look at new data in reg tech. T provides a new surveillance challenge. Regulators will need to have the tools and resources to keep pace changes. Thank you, again, for the opportunity to testify today, and i look forward to your questions. Thank you. Mrs. Wagner, youre now recognized for five minutes to give an oral presentation of your testimony. You. Agner thank chairman and Ranking Member<\/a>. Ts an honor to discuss the deployment of Artificial Intelligence<\/a> in the Financial Services<\/a> industry and our future workforce. Wagner, chief perating officer, our firm, were operating in 50 markets globally and together employ over 1,600 people. Advisory board, which is half women, promotes responsible innovation, including advancing Diverse Workforce<\/a> in our industry. S. Wegner over the past decade, weve seen automated trading leading much to the exchange t of the floor. You see in 1980s wall street movies. Echnology, as you guys have noted, has reduced the cost of trading for the average investor the past an half over decade. Both in direct trading costs and through tighter spreads. O if you are an investor in a 529 College Saving<\/a> plan, a 401k, then or a you have benefited from the in the y weve seen market. Over lifetime savings, investors have 30 mothers more in their 30 more in their bank accounts. There are four points i want to discuss in the oral testimony. First, global competition to latest a. I. Technologies will make human decisionmaking more efficient in speed, processing time, depth of data, and its going to and rm more efficiencies cost savings for u. S. Investors across the board. Competition in the markets have resulted to near commission, from fidelity, lotte schwab, and weve seen s schwab, and weve this cut by a fraction of the price from over a decade ago. Expect to see a proliferation of reg tech as a. I. Becomes increasingly for individuals, regulators to police the market efficiently. It includes monitoring, reporting, and compliance and of regulatory filings, loan origination processing, detection and and g of illegal irregular trading, and detection of cyber risks. I want to point out through Public Private<\/a> partnerships, firms can play a regulators ing with to share those limited resources in a. I. And to share cutting edge technology. 2017, several modern Market Initiative<\/a> members have with fen are an in public fenra in partnership. E we work to deploy artificial surveilence together to the markets. We can detect bad actors. Is become victims of fraud. As bad actors become more ophisticated globally, its vital that financial regulators have the Funding Resources<\/a> so to , too, have the access a. I. And others to be a cop beat. Third, as a. I. Technology matures, we can expect increased highquality, robust data, including alternative data to provide what i call the crude engines of a. I. This entails large quantitities f complex data that humans alone cannot digest. So i think were going to see questions arise around this proliferation of data. I think it was already noted competition and antitrust in the digital marketplace. Were going to see increasing intellectual Property Rights<\/a> and ownership rights of that data and access of that data. I think alternative data has been successful in helping history for redit the bank. Positive. I think we need to continue discussions running algorithmic bias. In my testimony i noted industryled initiatives to practices, use r officers. Workforce, a. I. And automation can and should be a replacement han a for humans. Some jobs will disappear and others will grow. Of growth we can expect to see are in the computer occupations, jobs related to the transmissions, storage, security, privacy and integrity of data. Industry. Ptics they are all going to be fueling the a. I. Economy. Massive demand for qualified, technological talent sectors of our economy, particularly in the financial economy. Women, particularly, women of color, is something that leaves for substantial improvement and thats something were focused on. A Skilled Workforce<\/a> for omorrows wall street is only as good as the companies that invest in technology. I thank you for your time and for the next witness. Thanks. Mr. Foster thank you. You are now recognized for five to give an oral testimony. N of your the button. Not the s microphone is very directional. Rotate it straight at you it helps. Thank you for the opportunity a. I. Stify on the impact on on our Capital Markets<\/a>. With eople associate a. I. Igh tech movies such as the matrix and the terminator. As you know, nasdaq has operated our markets and markets around to protect participants and investors. Rejsjo we have trading Surveillance Systems<\/a> to hundreds of markets, regulators, clearinghouses, and brokerdealers. Our internal Surveillance Department<\/a> is monitoring the market for insider rading and fraud manipulation as well as handling realtime. He increase of players, with their ability to deploy many strategies using their own technology and the exponential data quantitities can access the perfect ecosystem manipulators to hide amongst the noise. This increased complexity in monitoring presents new for the surveillance known factors. N our Surveillance Program<\/a> is sing algorithmic coding to detect unusual market behavior, algorithms fferent in real time. In addition to realtime 150eillance, there are over patterns covering surveillance of dentify a wider range potential misconduct. It increased the quality of to meet nce and changing demands in the markets. But with the manner in which are recognized, relying on known factors to describe behavior, it can be difficult to behavior and to remain proactive rather than reactive to threats in the market. Addition, predefined expectations of what patterns go to large n results depending how theyre brighted. Calibrated. Led to a lenges the nasdaqon between echnology business and a team to enhance capabilities with the help of Artificial Intelligence<\/a>. Abnormal to detect behavior pattern is based on the otion that manipulative behavior can be identified by signals in the market. Defraud e to participants often have a specific pattern to it. An action is taken and the trading is back to norm. Concept leads to new ways to look at pattern detection. A. I. , detection is not tied to static logic or para meters. Able to train the machine based on initial patterns of start to on and we look at the spoofing pattern. The machine must then train with human input and then transfer learning was used to expand the beyond the project spoofing. Transfer learning a. I. To apply a specificeloped for task is the starting point for the second model. Using deep learning and the initial spoofing examples indicated usable than typically required. The inclusion of a. I. Into the allow us function will to focus on indepth manipulative on ehavior instead oftoriaging instead of triaging other things. E must train the machine to produce more and more accurate outputs. Data is a significant challenge for surveillance professionals. Millions of messages pass a larger market on an active day. Have become more sophisticated, putting more pressure on surveillance teams a data the needle in haystack. By incorporating a. I. , we arary capabilities and broadening our view to safeguard of intek rit integrity our Financial Markets<\/a>. Nasdaq is looking to apply this in other business. For example, were using a version of a. I. , natural language processing, in the to facilitate s the compliance review of Public Company<\/a> filings. Closing, we are convinced that the use of a. I. Will the it investors and resiliency of the markets we serve. The opportunity to testify and i look forward to your questions. Mr. Foster thank you. Myself for ecognize questions. I should also mention to the members present, it looks like for votes estimate are now 11 30. Have time for ct a second round of questions for members that are interested. Have to play that by ear. Lopez de prado, you said have a wide dors range of data sets. Other witnesses mentioned that. Things that were not available a couple years ago. Not only the data itself, but he Processing Power<\/a> to analyze it and the realtime delivery of that data is becoming more and successfully to trade on it. Us d you just eliminate to some of the more interesting ata sets you are seeing being used . Dr. Lopez de prada yes. Location data, satellite from ranskripgss ransdescription from earnings calls, it allows us to estimate if the deal fairs well. Fracking. All sorts of data. Keep in mind, please, that 80 all data recorded today was past three, four years. Going back to history, going mesopotamia, there is we did und, data that not know, from chats, so all his data can be used to understand what is psychology of people, state of mind of people, people are more relocate their instead fixed income of stocks. Withhat are the narratives fi companies. This will only increase, because he storage of data is becoming cheaper every day. And the Processing Power<\/a> is increasing. Definitely a trend thats not going to stop. R. Foster now as i think i mentioned in my opening remarks, that has a danger of driving monopoly. Returns to scale because you get more correlations to look at with your a. I. If you have the full range of data. Naturally cause those smaller players in the effective, t be as less profitable, and ultimately. Part of i think thats what youre saying in High Frequency<\/a> trading, the consolidation that youre seeing there. Is there any way around this . Hould we how hard should we lean against the natural endency to monopoly here in financial trading . Dr. Lopez de prada so there are thischools of thought with regard. Number one, there are a number that demics who believe this consolidation is not necessarily negative. N the sense that the few survivors that are able to consolidate, for instance, High Frequency<\/a> trading, today are utilities. They are not making the kind of returns that they were able to nine years or 10 years ago. Essentially what happens is they even. This technology is becoming so expensive. They have to spend their time and money in order to achieve a dwindling. s there is a number of academics consolidation at is not negative. On other hand, the problem that small number of perators could have could cause a domino effect if one of them fails to provide liquidity. A need to strike a balance between on one hand preventing too much and the other favoring a condition between operators. You oster ms. Wegner, mentioned that this actually being lect troin electronic trading netted out positively for someones account you se of the lower, know it offers spreads and think tion costs that i you quoted 30 more in your retirement account as a result of this. Similarly, when a. I. Is widely deployed, if its very deployed in principle, we have good Capital Allocation<\/a> across our country, best is actually the strategy to let a small number of very dominant players have to all the data set to economy e efficient nd or are we better off letting encourage we need to policy in the High Frequency<\/a> space. Weve seen fierce competition in decade or two, were pproaching near zero latency speed. Mr. Foster more mow nop lakes. To is something i intend return to. Thank you. Id also like to remind all the witnesses to speak as directly the microphones as close nd loudly as comfortable for you. I yield five minutes to my the Ranking Member<\/a>. Mr. Chairman. Ms. Wegner, as you know, the experienced some cybersecurity difficulties, edgar lly in the 2016 data breach. I think its important for the only obtain roprietary trading algogh rit ms if absolutely algorithms if absolutely necessary. I want you to tell us why source needs to be protected . Lifeblood the real of automated trading is the intellectual property competing are against. Just like a selfdriving car algosy needs to keeps its and source codes, intellectual and so do algorithmic traders rely on Government Protection<\/a> for their intellectual property. A proposal a number of ears ago to perhaps collect i. P. Source code and put that in a government repository just in case that was needed. This is something were educating policymakers on. A need to make sure we have globally competitive marketplace intellectual property. Mr. Loudermilk i appreciate that. My time in the military and we had a principle we lived by because of the we itivity of data intellected and collected and main stayed, if you if you dont have something you dont need to protect it. Concern is how vulnerable the quite y becomes because frankly, the government tends to be the weakest link when it Data Security<\/a> in some aspects. I think obtaining that source only just a violation of the privacy right the coder, it , could be a National Security<\/a> risk. That ms. Wegner i think thats right. F bad actors were able to breach the source code, it would be presenting an opportunity for manipulating the markets or cyberrisks. Right we need to protect it. R. Loudermilk ms. Fender, technologies also create a need in other orkers fields. Today, we have about a million in the airline ndustry, but in the early 1900s, the Washington Post<\/a> led a headline, man will never shouldnt. Part of their argument is the the acement of people in job market. Could you touch on the job growing because of the use of a. I. In the Capital Market<\/a> space . Fender thank you. Many noted, there are ways that jobs are training. Key. Ation is really when we surveyed industry leaders, those doing the hiring, what are the most important skills Going Forward<\/a> not necessarily the job description. The skills underlying who will succeed in the future . And they talked about something tshaped skills. This is the idea if you think about the letter t, you got the bar where there is deep subject matter expertise and a orizontal bar where you cut across different disciplines. If you think about fen tech, if a big over here. If we have fin over here and tech over here and not talking, ability to connect the two there is a lot of opportunity. These are the innovators. Area where you see more research being done so we understand what the trends are. That people is have to ask the right questions. Firms are realizing, you have to about the why of gathering of this data. Many of the Machine Learning<\/a> will say, a large percentage of the data is not useful. How to do be smart that and start the process with investment professionals. Ok. Loudermilk so getting at is not all the jobs are going to be just as deep intellectual, being able to code and understand algorithms that come re jobs about because of it. Dont think that c. F. A. Charter holders will be programmers. To speak the language and Work Together<\/a>. Ok. Loudermilk want to talk about the use of Artificial Intelligence<\/a> and fraud detection. The w cybersecurity as biggest challenge we face in this nation, both from a business, government, and perspective. Could you touch real quickly, how ng out of time, algorithms are used to detect behavior. Rket yes. We rely on algorithmic coding to patterns that we see. I mean, everything needs to be compared to something that is right . So we program things to pick up n the unusual things based on historical comparison on specific stocks, how they have been trading in the past. So thats what we do already, and we have done for a long time. Rejsjo and mr. Foster hopefully well lead it to the second round of questioning. Adams is recognized for five minutes. Ms. Adams thank you very much this e chair for putting together. We appreciate it. Also the witnesses. Automation technologies which task of human labor machines, specific Industries Like<\/a> credit lending and capital are beings affected by a. I. As human tasks involving decisionmaking and compliance are replaced by robots. Learning the shift in job automation can predict which jobs in financial will be replaced and created. Could be ms. Wegner, specifically underwriting compared to the traditional ethods of meeting a loan application in person, to what or nt does a. I. Replace augment those done by credit ounsellors or other credit underwriters . Ms. Wegner in the consumer context, a. I. As a tool for humans, whether theyre credit and loans, there needs to be systems in lace to make sure there is no algorithmic bias. In my prepared testimony, i noted some suggestions. Not engaged in the Consumer Lending<\/a> context but insight. Ur own i think Loan Companies<\/a> individually or collectively could employ ethics officers to algorithmic is an bias in the lending context. Think its important that industry members share lessons how theys they explore are democratizing access to credit and finding the most extend that s to credit. I think its really vital we act now to make sure as were those systems that we minimize the risk for lgorithmic bias and Consumer Lending<\/a>. I think its very vital. Ms. Adams is the u. S. Properly remain competitive in the Financial Services<\/a> orkforce, this question is to dr. Lopez de prado and to ms. Fender. Lopez de prado the United States<\/a> is the leader in the inancial Services Industry<\/a> today. My concern is leadership is being challenged by the fact we are not hand as sting as much in a. I. Other countries. We are educating our competitors. In my remarks, i mentioned that concerned that the innovators of the future are our ding today a class in universities but they will not be allowed to stay. We are very yes, competitive and this to etition this ability train these skills will turn we are not able to retain this talent. Fender. S ms. Ms. Fender we have seen that, for how s early days 10 using with these techniques, but what we are seeing is that firms are labs. A. I. Theyre doing innovation hubs. They realize this is something proactive about. And so we are seeing out of our case studies, you know, we had a criteria that things in the in that things that are actually in practice, five of the nine are here in the u. S. Adams thank you. Adequately are we teaching the skills needed for the future . Cilwain i think were adequately teaching those skills. Access tion is who has to that teaching . The underrepresentation of members ndividuals and of the workforce who are not getting the types of education are needed for the jobs that may be coming online as a automation and a. I. Think if weand so i were to have a full pipeline of are able to receive what it is that we teach in our olleges, universities, even high schools and younger, then we have to be more proactive making sure that all people have access of that teaching and that information. Ms. Adams no one left behind . Dr. Mcilwain absolutely. Adams ill yield back. Mr. Foster and the gentleman from indiana, mr. Hollingsworth, is now recognized for five minutes. Mr. Hollingsworth this is i appreciate your comments because what you have touched on is something i have been an ardent believer and that is number one that the big arm of the federal government isnt going to stop the growth of this technology and isnt going to stop the investment in a. I. And while we can shape the context by which that technology flows we arent going to dam up and stop that technology and job losses may account, there is a lot of fear and desire to put an end to that, but i like how you reference training that needs to happen and trainees to ensure they have the skills for the 21st century workplace and those already in the workplace have the retraining. And we see further growth in development in a. I. , it will require more and more frequent retraining to stay relevant in that field. The second thing you touched on is something you are even more ardent about is we educate a lot of kids in this country. We do Higher Education<\/a> better than anybody else in the world. We invest a lot in those kids and we asked them to leave. That is embarrassing and idiotic and stupid. Want to find a way to retain talent. I believe this country can provide Technical Development<\/a> and i think technology will benefit humankind overall and i want to make sure we do that and i appreciate you touching on those topics. Ms. Wegner, you have a source code event yesterday, today, tomorrow, ms. Wegner this afternoon. I appreciate you continuing to educate a lot of people. Where i go across the district in indiana i hear how much investment, how much i. P. Is what we are seeing either in businesses processes in the source code. Underpinning automation and i know how important it is and i appreciate you bringing that to light. I wanted tore ask rejsjo. I had some people in my office earlier this week that were complimentary of Nasdaq Surveillance Services<\/a> and Public Companies<\/a> and how when something seems amiss in the markets, nasdaq was quick to pick up the phone and says something seems amiss. One of the things that is important back home is biotech. People dont know that and we are trying to get the word out and they are promoting this idea there should be more disclosure around short selling similar to long positions. Disclosure around short selling would help us better understand those who might have interests adverse to us because we cant track to that. But nasdaq is doing a good job of figuring when there is potential manipulation. Is disclosure in short selling that would benefit the market and the firms or do you feel you. Ave the ability to track i just want to make sure that its legitimate action and not market manipulation. I wonder if you could comment on hat. Ms. Rejsjo the information is always needed to understand what is happening. I do think what we have today is sufficient. We have a lot of patterns that are detecting the short selling. R i might say the double sum so its really to detect what is how it is used in an unnormal way. You feel you can detect the activity that would be illegal, abnormal or different, what do we do after that point is maybe where we should focus Public Policy<\/a> attention, is that fair . Ms. Rejsjo there are other parts that nasdaq has more of the policy questions. I do think that what we have the tools are adequate. I think thats an important question. Thats the question, where do we need to focus Public Policy<\/a> question and focus on the enforcement agencies. With that, i yield back. Mr. Foster one of the areas of bipartisan agreement is the insanity of this business of people wearing their diplomas and pushing it back on the airplane i was proud to introduce h. R. 4623 to keep stem talent act of 2019. Its a rifle shot to exactly solve this problem and i look forward to my colleagues support on this. I recognize ms. Garcia from texas. Gash gash thank you to all the witnesses. Good morning and welcome and wanted to focus on a couple of issues that some of you have lready talked about. We are concerned about jobs and im encouraged that you have the onsensus there will be job displacements and new jobs created and whether or not we do to transfer sets those skills or make sure that we can fill those jobs. Because in the end thats what matters to families in my district. But im concerned with our nation and the difference between a. I. And automation and how we can Work Together<\/a> specifically in the areas of Regulatory Compliance<\/a>. Ms. Fender, your experience, has a. I. Automation affected Regulatory Compliance<\/a> . Is it work in progress or how are we doing . Ms. Fender thats a very good question. Its still early to know. We hear so much about what is coming and yet, the compliance areas are growing in firms, clearly. And we have more and more data. And regulators are going to be able to have the same sort of data. Is there a greater risk of insider information. We collect more date after and people can see lots of different patterns and if they see it and trade on it before the markets, then you have challenges. To ensure they have those resources and Congress Explores<\/a> and have the resources they need because the systems are becoming much more complex and regulations that are evolving and need to keep up with the pace of technology. Those bad actors as you describe them. Are an a. I. Assist us with we well prepared for that . We did several countries and things are getting more and more sophisticated and bad actors and Better Things<\/a> to find ways to hide the money. Do we have what we need to detect it and ensure that we can catch it . Its vital we focus on it. The new head of innovation has an excellent group. They established themselves this year and working together with other regulateors and to gather information about best practices and make sure we have the best technology. This is 100 we need to be focused on. In your opinion, do you think that our regulateors and our oversight entities are well prepared in this arena or what else should be we be doing . There is always more room. And there has been a positive example of the s. E. C. Using sophisticated tknoling. This is a constantly evolving space. Keept to keep very much on investing in this area. Did you want to add something . I think the more date after we have, the more complex we get. And one of the things we are concerned is the Investor Protection<\/a> side. Nd if bad data goes into these models, they can be marketed, so disclosures are very important. Understanding a client and understanding where the money comes from and what clients are getting, it all goes together. I yield back. Foastfoast the gentleman from virginia is recognized for five minutes. Mr. Rigell im not showing any favoritism. I would like to welcome ms. Fender which is located in my district. It provides a host of institutions who are amongst the highest qualified. I am horpped to have a distinguished group residing in the 5th district and even though she isnt a constituent. Welcome to all of you. But can you talk about c. F. A. Is . Apting the charter to a. I. Pleased to be here representing virginia. The institute is the global standard for investment practitioners, the people that have our Portfolio Managers<\/a> for your 401ks and chief Investment Officer<\/a> and people that are safeguarding the Financial Futures<\/a> of so many people. And it is imperative to keep up to date on what we teach. We just added Machine Learning<\/a>. And this is a significant indication that we are seeing the market change and we need to prepare people. We have a group called our Practice Analysis<\/a> team and out there all the time going to these conferences and figuring out what people need to know because Global Demand<\/a> is growing and especially for those who combine both competence and ethics. Mr. Rigell my prior job and we talked about data. Wanted to monopolize for data interactions when i worked for the office of secretary of defense. We had to look at all data across stovepipes and analyze that data and analyze and using that data based on Human Behavior<\/a>. Looking at a. I. , i will start ith rejsjo and this is exciting, do you see when we did this, we had multiple data sets and challenges of data. We had multiple data sets and we had data aggregated and combiped with other data sets and thought we had the right answers. Is that something you are going to see more in the future. And or Machine Learning<\/a> rules to sort of mimic what Human Behavior<\/a> does with rule sets . And any fraud or anything of that nature . Ms. Rejsjo we are a long way. For now the way we do it is to really have the data that we have. Its the order that we already. And applying new techniques to give us more better overview. Mr. Rigell this is a tough question and being objective in 40 seconds is probably ridiculous. When you are looking at this and i know this is a tough question, with all the proprietary questions out there, is there voluntary sharing across multiple sets . Another company. Do you think we will have solutions peaced on those analysis or do we have to force that to happen when we monopolize that data. Mr. Deprado when you look at the model. There has been a lot of transfer between the agency and various contractors. That could be a model that could work. In particular, in my remarks, i mentioned the outsourcing of investigations and companies or private participants could establish tournaments who have agencies identify market manipulateors. Mr. Rigell i yield back. Mr. Foster the gentleman from illinois is recognized. I had back in my prior life, i had a head of engineering and said every advance in technology and us more precision and 16 significant digits. And you know, in my lifetime going from maps to g. P. S. And longitude and latitude and i cant tell you if i am north ks south, east or west. That is acceleration on steroids. And i built the predicting the receive news of our business. I cut our revenue experience by 40 and i have no idea how it worked. D thats the power and the frustration. And i mention that because most of you have talked about the consumer benefits when we get all out in the and the terrific. The question i have and a lot of you talked about bad actors and put up monitoring for that. The concern i have is the tension between the transparency of the model and whether the model can actually replicate a bad actor we dont understand. Understand a trading algore rhythm. And making money. And changes in trading flows that is not but is in some spread that results from that. So i wonder if you would comment on that tension between transparency and robustness and what need we need regulatory tools to stipulate on where we stipulate . Ms. Wegner transparency is absolutely vital. And have the resources and any sort of irregularity in the markets, they can identify that and to the question of whether or not one needs to get source code, if there is a detection of irregular activity if i could clarify, would you agree that the more transparent, the less powerful . I think to the extent that it is not subject to intellectual Property Rights<\/a>, that transparency is absolutely vital. If were talking about intellectual Property Rights<\/a>, that is proper pro tear information. Im not referring to whether or not the public has access to it. Im referring to whether our human brains can understand how it works. I could give you the genetic, you couldnt understand what it is doing. We deploy black box solutions. These solutions and identify patterns that are not real. And compound these patterns. Actual signal. Leading to his fail. And we need to understand when a product is on a black box solution. I yield become. I would welcome followup comments. Foastfoast likely to have another round. Mr. Cleaver is the chair of the subcommittee on National Security<\/a> and International Development<\/a> and Monetary Policy<\/a> is recognized for five minutes. Mr. Cleaver really appreciate you calling this hearing and appreciate all of you giving us your time. I dont know how we are going to. Eal with a. I. And human beings phones, re we had flip captain kirk had one and long before we had the smart watches, mr. Spock had one. Nd and a lot of attention is always paid to hollywood particularly in Science Fiction<\/a> and the military, our own military. And so, a lot of people have their eyes on a fearful future s it relates to a. I. And to be straight, im one of those. Im conflicted. We cant hold back the wind and it is inevitable we will see more and more of this in the future and im not sure that we ought to try to hold it back to the degree that we can control it, thats what i think we ought to do. And thats what im concentrating most of my interest. Doctor, thank you for being here. How inclusive ng this new technology is right now and what can we do to make sure that in the future that every component of our great most aic in the United States<\/a> is a part of it . Mr. Mcllwain i share a little bit of your fear because what we know persists as Technology Changes<\/a> as technological advances that some people and some groups of people are left out and left behind, disadvantaged and even as technology is unpredictable, some of those exclusions are very much predictable. And i think those exclusions are present in our current market as most of the folks on this panel have at least alluded and nodded to, ideas we look at our Technology Sector<\/a>, those who are prepared to be part of that sector, those who are currently working building the technology of today and tomorrow are tremendously unrepresentative of our full democracy of all the citizens of our country. And i think representation makes a tremendous difference. I think the place were in today with respect to some of the inaqualities and devastations that technologies, a. I. And automation included have weakened because no everyone is in included the decision who they will advantage and disadvantage and moving forward, we have to change that, we have to invest strategicically in building a more Inclusive Work<\/a> force in these sectors that are growing, that is the Technology Sector<\/a> and Financial Sector<\/a> as well. Mr. Cleaver what do you think we should do or any of you do right now i mean we got to have young people interested in and ommitted to the future a. I. Is an inevitable part of it. What should they do next week . How should we direct young people right now who are scientifically gifted . What should we do . We need to promote responsible innovation. Our members are getting out to the middle School Population<\/a> and get them interested in stem fields and a lot of companies to partner with the public schools. And help fund that and just recruit now. Kids get interested in these fields and we have to get in there early and people see this as we promote privatepublic partnerships. Foastfoast we have time for a brief second round of questions here. We have had two different fartives that have been going on here, one is the optimistic narrative, the tshaped schools or human intelligence pairing and there will be augmented human intelligence and we have the intermediate way of transfer learning. You use one field of expertise and transfer what was learned there to another field thereby replacing multiple machine pairings. E example of that was at the to predict analyzing cement pricing in the future. And then potentially using transfer learning so that knowledge could be transferred to Copper Mining<\/a> or whatever it was. For human participation in this. Anyone who wants to tackle that tar baby. I can start. One of the foundational concepts in investing is that correlation is not necessarily causation. We have a lot of data and see these patterns that you need a human to ask what is the right question. I give the example of going through the news stories with bloomberg and they said the question is go through the news article and not think what the author wanted to get across but what is the author thinking. Thats why again, having a collective intelligence and diverse perspective, its going to be important. Mr. Deprado the two fartives have some part of truth. We have some reasons to be worried about the transfers of knowledge as these technologies are more broadly deployed. But in the long terms we have reasons to be optimistic because the next generation will be better prepared. It is very important that with this access to education and equal access to education and we encourage people to learn how to and the free form Flexible Work<\/a> force. And works in the future. And they will be able to engage proactively. Foastfoast is this going to squeeze the financial profitability. And if you had them trading on that knowledge, the 30 improvement in your retirement savings, all of that money used to end up in the pockets with people with nice homes on oyster bay. And that is the nature of things and it may be when we get this extensive deployment of a. I. , the total amount of money left to be extract will go down the same way that high trading. And Financial Adviser<\/a> and receive the same treatment we go to the doctor. This is what you need to invest in order to achieve your retirement goals, i think that is a good outcome. And other more efficient i would say half the managers will deploy that efficiency. But i think it raises the global competition question. We are talking about internationally and not stop time across the world. Other countries are innovating in a. I. We will be competing in that space and keep the u. S. Envy of the world. Foes foes mr. Foster is the objective a function that the a. I. Running alexa, is that amazons profit r linear and diversity inclusion, secure retirements rather than steering people into products that are profitable for amazon . There is not too much aggregation of power. We need policies that promote it this is an exciting space and meets on commerce issues. And finances are becoming more technology. These are the right questions. Mr. Foster i yield five minutes to the Ranking Member<\/a>. Mr. Loudermilk i would like to continue our conversation that we were talking about, cybersecurity and using a. I. And wasnt managing that time before. Could you explain further how nasdaq is using a. I. In fraud detection . It is more just to start, the future is here, right. We have billions of data points and massive amount of data that needs to be analyzed to capture ta is fraudulent. So we have that environment already and what we have been doing so far is having coding to be able to process this data very fast. Our realtime is within seconds. So there is really a fast and efficient way to go through the data. But it is growing and there is the need to continue to invest in other ways of looking at it where a. I. Comes in. We can capture more things that are sophisticated. Can you touch on the difference between High Frequency<\/a> and Computer Trading<\/a> and what differenceiates each of those. Mr. Deprado computer follows sole rules and does not require much in learning. Machine learning is the learning patterns from a set of data what happens is, and the data of. Nd we were not aware what was it . The automated high trading. Mr. Deprado it can happen with or without Machine Learning<\/a>. High frequency trading. What we see is providing the Market Makers<\/a> deploy High Frequency<\/a> solutions. Mr. Loudermilk we have had some discussion on the cost savings that have resulted from a. I. And capitalization. Do you see these are a ignificant reason. Ms. Wegner this has contributed. Every reduced of incremental trading. So as the markets become more efficient, investors will have more in their pocketbook. Invested in 529 plan. Mr. Loudermilk i have no further questions. I yield back. Mr. Foster the gentleman from missouri is recognized for five minutes. Mr. Cleaver im interested in how do we do planning now for the future. Just updated our antiMoney Laundering<\/a> bill in the Bank Secrecy Act<\/a> and i introduced the bill. I have been feeling pretty good about myself until you guys came p today and im thinking, what why did we go through all of that because the bad guys are trying to figure out how they can exploit whatever we pass legislatively. Ow do you see a. I. Involved in antiMoney Laundering<\/a> efforts like the legislative efforts that we will take up during our lifetime. Is there any way that you think can play a role, a. I. Can play a role in our Money Laundering<\/a> bills or how we are trying to reduce it . We know we will never eliminate it. Mr. Deprado we have to tackle tremendous amounts of data and identify this needle in the haystack. The practical solution for regulateors to Work Together<\/a>. Entire community. We need to annual idse this data and give this data to the communities so the communities help us enforce the law. That is a very doable approach knowing how difficult it would be for the agencies to develop the techniques that the wrongdoers are developing and number two, the amount of data we need to go through. Mr. Cleaver we had the treasury secretary before our committee yesterday. Its an investigatory part of department of treasury. Im wondering what theyre doing trying to keep up with the technology and what challenges they are going to face in the future. And so you guys have destroyed almost everything i was proud of but i appreciate you coming here. I yield back. Mr. Foster i would like to thank the witnesses. All members may have have five legislative days to submit questions and forward them to the witnesses and i would like to ask our witnesses to respond as promptly as you are able. All members may have five legislative days to submit extraneous materials to the chair. And this hearing is now adjourned. [captions Copyright National<\/a> cable satellite corp. 2019] captioning performed by the national captioning institute, which is responsible for its caption content and accuracy. Visit ncicap. Org cspans washington journal, live with news and policy issues that impact you. Coming up this morning, making gisela discusses the status of the u. S. Mexicocanada trade agreement, then seth siegel talks about the safety of Drinking Water<\/a> in the u. S. Electric assist consumer testing kits and privacy concerns. Washington journal live at 7 00 eastern this morning. Join the discussion. And now, more about next weeks congressional agenda with more georgian leader steny hoyer and majority whip Steve Scalise<\/a> minority whip Steve Scalise<\/a>. They came to the house floor and spoke about surveillance on congressional members and the impeachment process. This is half an hour. Remarks. The speaker pro tempore without objection. I would be happy to yield to the gentleman from maryland","publisher":{"@type":"Organization","name":"archive.org","logo":{"@type":"ImageObject","width":"800","height":"600","url":"\/\/ia803102.us.archive.org\/4\/items\/CSPAN_20191207_070400_Hearing_on_Impact_of_Artificial_Intelligence_on_Capital_Markets\/CSPAN_20191207_070400_Hearing_on_Impact_of_Artificial_Intelligence_on_Capital_Markets.thumbs\/CSPAN_20191207_070400_Hearing_on_Impact_of_Artificial_Intelligence_on_Capital_Markets_000001.jpg"}},"autauthor":{"@type":"Organization"},"author":{"sameAs":"archive.org","name":"archive.org"}}],"coverageEndTime":"20240716T12:35:10+00:00"}