Transcripts For CSPAN2 Hearing On Impact Of Artificial Intelligence On Capital Markets 20240713

Next, financial analysts review the impact of Artificial Intelligence on Capital Markets. House Financial Services subcommittee examined ais impact on minorities. Cyber security measures, encouraging more young people to Study Technology and how the us can remainglobally competitive. The task force will come to order. Without objection the chair is authorized todeclare a recess at any time without objection. Members of the full committee not on this task force are authorized to participate in todays hearing. This hearing is entitled robust onwall street, the impact of ai on Capital Markets and jobs and the Financial Services industry. The chair will now recognize himself or five minutes or an Opening Statement area thank you all for joining us today for what should be avery interesting hearing of this task force. Today were looking at exploring how ai is being deployed at Capital Markets from automated trading to Portfolio Allocation to Investment Management decisions. Were going to consider how the use of this technology is changing the nature of work in Financial Services and rendering some jobs obsolete and changing the skill sets needed to excel in others. It would not be much of an exaggeration to say wall street quite literally is run by computers. Long gone are the days when traders would be screaming orders on the floor of the stock exchange, financial analysts would use the ical chelators and pour over the tickertape to glean insights into a companys value. I hear about those days from the limo driver who takes me back used to be a floor trader on the market. Today, trades are automated and orders are executed in milliseconds or microseconds. The path of ots relying on algorithm models to ensure the one holding and shares are properly weighted to whatever index or benchmark its tracking. Hedge funds use algorithms thats. Scour all sorts of market data to find the stocks and have the most momentum or highest dividends or look for correlations that will in the market and an external data feeds to provide the most now you for investors. And i think its notable that a lot of the shakeout that were seeing in those markets is really a question of serving winner take all nature of digital economies that any Digital Business purely Digital Business is an actual monopoly and is as more plans become digitized you will see more of the rewards go to a smaller number of dominant players. Id like to emphasize it doesnt mean theyre evil, its simply a natural reflection of the nature of the digital marketplace. Other Asset Managers may use algorithms to form complex research and analysis in realtime on big data sets this can include towering of social media sites, satellite information, web traffic, onlinetransactions and just about anything else you can think of. This is i guess good in terms of having the market reflect all known data but there are abusive corners. Imagine what it would be worth if you had a 10 second early look at trumps twitter feed. How much money you canmake trading off that for example. The three types of computer managed index funds and quantum funds make up 35 percent of the approximately 31 trillion American Public equities market area human managers such as trench funds and other managed 24 percent of the market. The rise of the computerization of our stock market has a number of benefits, the cost of executing trades has gone down sometimes to zero dollars and theres more liquidity in the market. Funds charge less than one percent of management while active managers charge 20 times thatmuch. It certainly creates additional questions as well. As in the 2010 last crash and the more recent many flash scratches have shown Algorithmic Trading can sometimes cause unpredictable consequences that create market volatility and also exacerbate information asymmetry between different types of investors as firms with more and fasteraccess to enormous data sets are able to obtain a competitive advantage. Another broader question is how these developments are impacting the nature of jobs in the Financial Services industry. Recent Wells Fargo Research report estimated technological efficiencies would result in about 200,000 job cuts over the next decade in the us banking industry. While these cuts will certainly affect backoffice call center and Customer Service positions the pain will be widespread. Many frontoffice workers such as financial analysts could see their headcount drop by almost a third according to a mckinsey and Company Report released this year. The report found that 40 percent of existing jobs and Financial Firms could be automated with current technology. If you spend your whole day staring at a big screen and particularly if youre receiving a large paycheck your job will be at risk area understanding the skills that will be needed to excel in the Financial Services industry and how we can encourage these skills is one of the issues that we must tackle head on and tackle early area in a world where many functions can be done by automated ai models, what role did that leave for humans . I look forward to hearing from our witnesses on these issues with that i like to recognize the Ranking Member my friend from georgia mister loudermilk for five minutes. I want to thank each of our witnesses here today. Thank you for taking time to be here to discuss this issue while the rest of america is fixated on other things going on here. This is something that may not resonate on themajor networks but it is something thats very important , has an impact on our lives positively but also potentially negatively and its important we be looking into this. And as you note today the task force will examine the intersection between technology and the Capital Markets. In recent years there have been many Technological Developments including the adoption of Artificial Intelligence and automation that have redefined and reshaped trading and investing. The first trades on the New York Stock Exchange were made in the late 1700s using a manual paper intensive process. For many years buyers and sellers communicated about orders over the phone. Today trading and investing are done on digital platforms and investors can trade securities from anywhere in the world using modern technology. Trading has benefited the markets in many ways it has been positive for investors by leading to lower overhead and transaction cost which has contributed to investment returns over the last decade. Avril major asset managementfirms offer zero percent commissions which means investors can buy and sell stocks essentially for free and can capture more of the growth of their investments. This would not be possible without electronic trading area Digital Trading platforms provided investors with lowcost Financial Research and advice 24 hours a day using robo advisors. Electronic trading also makes markets more efficient by allowing faster searches for prices, better processing of large sets of data and more transparent price information. A proliferation of technology and lower firms barriers to entry, foster more competition and increase Market Access for investors. In addition to these core benefits there are many other cases of Companies Using ai to improve efficiencies in the Capital Markets in unique ways. For example some Clearing Companies are using ai to optimize the settlement of trades and enhance Cyber Security and fraud protection. Some selfregulatory organizations are also using ai in market surveillance while there are many benefits to electronic trading it can also present new challenges. One challenge which is at the forefront of our discussion today is the disruption of job markets. While the rise of automated trading has displaced many floor traders, Job Opportunities like code writing, cloud management, telecommunications, Data Analysis are growing area there is some concern highfrequency trading can contribute to volatility but new evidence suggests highfrequency trading does not increase volatility and can improve liquidity theres also some concern that firms dont have the latest technology , firms that dont have the latest technology could be competed out of the markets reared its important to keep in mind not all types of electronic trading are the same and i look forward to learning more from the witnesses about the differences between automated trading , Algorithmic Trading, highfrequency trading and Computer Trading and i look forward to exploring the legislative and regulatory issues in this space. One issue i think needs to be addressed is the protection of source code because algorithms are traders core intellectual property. They must be protected. We passed out a bill of this committee in the house on a bipartisan basis last time to ensure the Security Exchange Commission Issues a subpoena before obtaining these other rhythms rather than getting themthrough a routine exam andmister chairman, i hope we will be able to Work Together on a bill this congress. I think you and i yelled back. Thank you and today were welcoming the testimony of doctor mcilwain, vice provost for development and professor of mediaculture and communication at nyu. Doctor marcos lopez de prado, Cornell University and chief Investment Officer of true positive technologies. Ms. Rebecca offender, cfa, senior director of finance at charterfinancial analysts. Miss kiersten wagner, chief executive officer modern markets initiative. Miss martin rachel, head of affect market surveillance, Nasdaq Stock Market area witnesses are reminded that your oral testimony will be limited to five minutes and without objection your full written statement be made part of the record. Doctor mullaney, youre now recognized for five minutes to give an oral presentation of your testimony. Chairman foster and Ranking Member loudermilk, thank you for inviting me to testify. While my written remarks cover 14 areas focused on two. The implications of automation on the workforce and mitigating algorithmic discrimination. We have ample reason to be concerned about automations future in the Financial Services sector. First, the Financial Services sector is right or automation. Second, the sector is on the rise, the large number of workers will likely be be displaced in the Financial Services sector even if automation and Ai Development is projected to create new types of jobs area if all this is true and the cost of concern is clear. It lies with the fact that africanamerican workers and latin workers are unrepresented in the Financial Servicessector workforce. African americans , hispanics and asians take up 22 percent of the Financial Service industry workforce. Africanamerican representation in the Financial Services sector at entrylevel and seniorlevel jobs climbed from 2007 to 2015, less than 3. 5 percent of all Financial Planners in the us are black or lacking. African americans make up 4. 4 and hispanics 2. 9 of the securities actor. Asians take up just 2. 8 of the central banking and insurance subsets. My point is simple. Racial groups that are already extremely underrepresented in the Financial Services industry will be most at risk in terms of automation and escalation of development. This is especially true given the vast underrepresentation Latin Americans in the adjacent Technology Sector workforce. If we are to mitigate the likelihood automation will disproportionately and negatively affect those already underrepresented in the Financial Services industry, you must plan ahead long into the future rather than allowing the market to run its course towards predictable outcomes. Now to the subject of deterring algorithmic bias. Certainly one way to mitigate the algorithmic bias is to develop best practices by instructing and deploying algorithmic systems, providing more oversight from industry, government and nongovernment bodies are able to assess how much systems are used and the outcomes they produce area this includes Technical Solutions and make algorithms more transparent and mitigate against potential biases before such systems gain widespread use rather than trying to simply correct their effects once their damage is done but i want to emphasize that especially when it comes to mitigating the potential disparate outcomes that biased algorithms might have on individuals and communities of color, a simple reliance on technical face axis is not a complete solution. I want to end by drawing on the wisdom of mayor ruskin, a formal civil rights leader who had a sophisticated understanding of algorithmic systems as they existed in his time. He said today the unskilled and semiskilled workers the victim but cyber nation invades the strongholds of the american middle class as once proud whitecollar workers begin sinking into the alienatedworld of the american underclass and as the newborn meets the old poor we find out automation is a curse ,but it is not the only curse. The chief problem is not automation but social injustice itself. Take as a final example the findings from a recent National Bureau of Economic Research study titled Consumer Lending discrimination and the thin tech era. Their researchers sought to determine whether an algorithmic system could reduce discrimination in mortgage lending as compared to traditional face to face lending process. Their findings were mixed area the system gets discriminated 20 percent less but the process still discriminated against a large number of applicants. Further even though the algorithmic system did not on balance effeminate in terms of loan approval it did discriminate against black and latin users in terms of price. One of the key conclusions of the study states that both thin tech and facetoface lenders may discriminate through pricing stress, we are just scratching the surface of the role of Pricing Strategy discrimination and the algorithmic area of data use. In short algorithmic lending me reduce facetoface lenders and it is not sufficient toeliminate discrimination and lower pricing. Even with the aid of a fair accurate and transparent algorithmic system, racial disseminationprocess. Iq again for allowing me the opportunity to contribute. Thank you and doctor lopez de prado you arerecognized for five minutes. Chairman. Ranking member loudermilk and distinguished members of this task force. Its an honor to be asked to contribute today. As a result of recent advances in supercomputing and big data today Machine Learning algorithms perform tasks that until recently only computers can accomplish. One area as investments. For two reasons, some of the most accessible trades in history happened to be algorithmic area their decisions are objective, their producible and can improve over time. The distinct advantage is their commission enables cost reductions. A committed task includes order execution, construction, forecasting, Credit Rating and protection and it creates a number of challenges for over 6 Million People employed in the finance and insurance industry, many of whom will lose their jobs not because they will be replaced by machines because they have not been trained to work alongside algorithms. The returning of these workers is an urgent and difficult task but not everything is bad news. These skills and become more important than privileged upbringing or genders and other classifications should narrow. It could be a great equalizer. Retraining our existing workforce is of particular importance but it is not enough. We must make sure that the talent that american universities have contributed and develop remains in our country. The next google, amazon or apple is at this morning attending a class at one of our universities and what unlike in the past , these entrepreneurs are in our country on a student visa and they will have a very hard time remaining in the United States to help them. And as we help them they will return to their countries of origin. We their fellowstudents compete against us. On a different note i would like to draw your attention to two practical examples of fintech through Regulatory Oversight read the first embodiment of red text is the crowdsourcing of investigations. One of the most telling task facing regulators is to identify market manipulators among mountains of data. This is literally tasks like searching for a needle in a haystack. Our field, as a consequence, Financial Firms offer online tools and even large hedge funds fall constantly for this trap leading to investor losses. One solution is to require Financial Firms to record all involved in the development of the product. With this information, regulators could have the probability that Investment Strategy and this probability could be reported in the promotional material. Finally, i would like to conclude my remarks with a discussion of bias. Yes, Machine Learning algorithms can incorporate human biases. The good news is we have a better chance of detecting the biases in algorithms, measure that bias with greater accuracy than with humans. The reason is we can subject algorithms to a batch of randomized controlled experiments. Algorithms can assist human Decision Makers by providing a business of recommendation that human cans override. Humans can override, thus exposing biases in humans. Congress and regulators can help reap the benefits of this technology while mitigating its risks. Thank you for the opportunity to contribute to this hearing. I look forward to your questions. Thank you. Ms. Bender, you are recognized for five minutes to give an oral presentation of your testimony. Chairman foster, Ranking Member loudermilk and members of the task force, thank you for inviting me to testify here today. My name is rebecca fender, the senior director of the future of finance at caf institute, which is our thought leader platform. It is the largest Nonprofit Association of investment professionals in the world with 170,000 cfa charter holders in 76 countries. It is best known for its chartered financial analyst designation, the cfa charter which is a rigorous threepart graduate level exam. To earn the designation, charter holders must also have at least four years of industry experience. Cfa institute is a Nonpartisan Organization and seeks to be a leading voice in global issues of transparency, market efficiency, and investor protecti protection. Earlier this year, Cfa Institute published a paper on the investment professional of the future, examining the changing roles and changing skills of the industry in the next five to ten years. Among the Cfa Institute members and candidates we surveyed, 43 think the role they performed today will be substantially different in five to ten years time and it was greater 50 among Financial Advisors, traders and risk analysts. Another 5 dont think their role will exist by then. One of the catalysts is technology. Cfa institute sees the impact of technology on jobs and the investment industry as a pyramid. At the foundation, we have basic applications. Everyone will need to learn to do things differently. And they must be more comfortable using and understanding technology. Some people will face tech substitution, but many more will have their roles adapted. Are in the middle, there are specialist applications, where technology will enhance work. And at the top, there are hyperspecialist roles that will be less common but very valuable. This includes roles at firms and ai labs. Cfa institute believes the key to this evolution is ongoing learning. Our exam curriculum now includes material about Machine Learning and among the members and candidates we surveyed in our recent report, 58 have interest in Data Analysis coding languages like python. Similarly Data Visualization and interpretation are areas that more than half have expressed interest in. In terms of the role of Artificial Intelligence, in the investment industry, the organizing principle we see is Artificial Intelligence plus human intelligence, or ai plus hi. In these middle and top levels of that technology hierarchy, Investment Management and Technology Teams Work Together. Ai techniques can augment human intelligence to free investment professionals from routine tasks and enable smarter Decision Making. Professionals will spend less time finding and entering data and more time ensuring models are consistent with how markets work. Ai unlocks the potential of unstructured data and can identify patterns and information more efficiently than humans. Ai can amplify an investment teams performance but cannot replicate its creativity. In our recent paper, ai pioneers an Investment Management authored by my colleague, we have identified three types of ai in big data applications that are emerging in Investment Management. They are first the use of natural language processing, Computer Vision and voice recognition, to efficiently process text image and audio data. Second, the use of Machine Learning techniques, to improve the effectiveness of algorithms used in investment processes. And third, the use of ai techniques to process big data including alternative and unstructured data for investment insights. We find that relatively few investment professionals, about 10 , are currently using ai in Machine Learning techniques in their investment processes. However, here a few examples from our case studies of what the ai pioneers are doing. Goldman Sachs Research team is better able to analyze concrete companies in the Construction Industry by using data of 9,000 u. S. Queries. Second, the Data Science Team at American Century investments studied psychology textbooks to determine patterns of deception in children and criminals. They then applied Machine Learning to these patterns in earnings calls to determine where spin, omission, and blame are being used. Finally, bloomberg has had a Sentiment Analysis Product Available since 09 which analyzes the potential effect of news stories on valuations. They processed 2 million documents a day through their Machine Learning platform. This was alternative data used only by hedge funds at first, but now many of their clients use it. Just as the investment industry is beginning to employ greater technology, regulators can look at new data in the world of reg tech. The speed and volume of data presents a new surveillance challenge. Regulators will need to have the tools and resources to keep pace with changes. Thank you again for the opportunity to testify today. And i look forward to your questions. Thank you. And ms. Wagner, you are now recognized for five minutes to give an oral presentation of your testimony. Thank you, chair foster and Ranking Member loudermilk and members of the Ai Task Force it is an honor to discuss the role of automation of the markets and our deployment of Artificial Intelligence in the financial industry and future workforce. Im chief executive officer of modern market initiative. We are an organization comprised of automated trading firms operating in over 50 markets globally and together employ over 1600 people. Our Advisory Board which is half women, promotes responsible innovation including advancing a Diverse Workforce in our industry. Over the past decades, we have seen automated trading leading to much of the replacement of the exchange floor, based intermediaries. You see in 1980s wall street movies. Technology as you have noted has reduced the cost of trading for the average investor by more than half over the past decade, both in direct trading costs and in savings through tighter spreads. So if youre an investor in a 529 College Savings plan, a pension fund or 401 k then you have benefitted from todays low cost trading and all the dependable liquidity we see in the markets. Studies have shown over lifetime savings investors have 30 more in their Bank Accounts as a result of that automation. Now, as we look ahead, there are four points i want to discuss here in the oral testimony. First, global competition to adopt the latest Ai Technologies will make human Decision Making more efficient in terms of speed, processing time, depth of data, and it is going to confirm more efficiency and cost savings for u. S. Investors across the board. Already competition in the markets has resulted in near zero Commission Online trading from fidelity, Charles Schwab and robin hood and we have seen a ride in the etf industry from those efficiencies. Similarly, automated trading has brought down overall trading costs to a fraction of the price from decades ago. Second, we can expect to see a proliferation of reg tech as ai becomes increasingly valuable for individual firms and regulators to police the markets more efficiently. Ai functionality in reg tech includes monitoring, reporting and compliance and processing of regulatory filings, loan origination processing, detection and reporting of illegal and irregular trading and detection of cyber risk. Notably i want to point out through Public Private partnerships firms can play a role in working with regulator to share those limited resources in ai and share cutting edge technology. Since 2017, several have welcomed the opportunity to Work Together in Public Private partnerships, contributing our knowhow, welcoming deploying Artificial Intelligence together to surveil the markets. So automated trading firms are incentiveized to detect bad actors because we too can be the victims of fraud. As bad actors become more sophisticated globally it is vital that regulators have the Funding Resources so they have the technological capacity and access to ai and Automated Technologies to be a strong and effective cop on the beat. Third, as Ai Technology matures, we can expect increased demand for high quality robust data including alternative data to provide what i call the crude oil for the engines of ai. This entails large quantities of complex data that humans alone cannot digest. I think were going to see policy questions arise around this proliferation of data. I think it was already noted questions of competition and antitrust in the digital marketplace. Were going to see increasing discussion of intellectual Property Rights and ownership rights of that data and questions of access to that data and the cost of data. I think alternative data has been successful in helping establish a Credit History for the underbanks. Thats one positive, but i think we need to continue discussions, writing algorithmic bias and in my prepared testimony, i have noted next steps including industryled initiatives to share best practices, utilize ethics officers, and reg tech approaches. And last, i want to talk about future of workforce. Ai and automation can and should be a tool rather than a replacement for humans. Some jobs will disappear and others will grow. Areas of growth we can expect to see are in the computer occupations, jobs related to the transmission, storage, security, privacy, and integrity of data, the fiber optics industry. They are all going to be fuelling the ai economy. Theres massive demand existing for qualified technological talent across virtually all sectors of our economy, particularly in the Financial Sector. The current baseline participation for women and particularly of women of color is something that leaves room for substantial improvement, and thats something were focused on. And a Skilled Workforce for tomorrows wall street is only as good as the companies they are to invest in technology. I thank you for your time and yield the time for the rest of thanks. Thanks. You are now recognized for five minutes to give an oral presentation of your testimony. So my mic is not working. The button . The microphone is very directional. If you rotate it straight at you, it helps. Thank you chairman foster and the Ranking Member for the opportunity to testify on the impact on ai on our Capital Markets. Many people associate ai with hightech as determine [inaudible]. As you know, theres extensive experience leveraging technology to operate our markets and markets around the world to protect participants and investors. We operate 25 exchanges and six clearing houses around the globe and we sell marketbased technology, trading clearing and Surveillance System to hundreds of the worlds markets, regulators, exchanges, clearinghouses and broker dealers. Our internal Surveillance Department is monitoring the markets insider trading, fraud as well as handling realtime events in the market. The accessibility of the markets and the increase in players with the ability to deploy many strategies using their own technology and increase in data quantities can act as a perfect ecosystem for market manipulators to hide amongst the noise. This increased complexity in monitoring presents new challenges for the Surveillance Team relying on preconceived and known factors to detect manipulative patterns. Our program is using algorithmic coding to detect unusual market behavior running over 40 different algorithms in realtime using 35,000. In addition to realtime surveillance, over 150 patterns covering surveillance identify a wider range of potential misconduct. The team proactively develops tools and procedures to increase the quality of surveillance and meet changing demands in the market. With the manner in which patterns are recognized, relying on known factors to describe behavior, it can be difficult to capture new behavior and to remain proactive rather than reactive to threats in the market. In addition, predefined expectations of what patterns look like can often limit results depending on how alert they are calibrated. Calibration presents a continued challenge when determining the best balance between falsepositive and true alerts. These challenges led to a collaboration between the Machine Intelligence lab, Nasdaq Market Technology Business and nasdaq u. S. Surveillance team to enhance surveillance capabilities with the help of Artificial Intelligence. Using ai to detect abnormal behavior pattern is based on the notion that manipulative behavior can be identified by signals in the market. It often has a specific pattern to it. It is price rise or decline, an action is taken and the trading is then back to norm. This signalling concept leads to new ways to look at pattern detection. By leveraging ai, detection models are tied to static logic or parameters. Were able to turn the machine based on visual patterns and we started to look at the spoofing pattern. The machine was then further trained with human input and then transfer learning was used to expand the scope of the project beyond spoofing. Transfer learning leveraged ai to apply model developed for specific tasks as the starting point for a model on the second task. By using deep learning and human in the loop techniques the new models for detecting market abuse with the initial spoofing examples indicated usable results with 95 fewer examples than typically required. The inclusion of ai into detection function will allow us to focus effort on in depth investigations on potential manipulative behavior instead of triaging a high number of falsepositives. To be clear, the human input is still of critical importance, both in analyzing the output from the Surveillance System, but also in continued training the machine to produce more and more accurate outputs. Domestic growth in market data is a significant challenge for surveillance professionals. Billions of messages pass through a larger market on an active day. In addition, market abuse attempts have become more sophisticated putting more pressure on Surveillance Team to find the needle in a data haystack. By incorporating ai, we are sharpening the detection capability and broadening the view to safeguard the integrity of the financial markets. Surveillance is a critical use case for ai but nasdaq is looking to apply it in other business. For example, were using a version of ai, natural language processing if n the listings business to facilitate the compliance review of Public Company filings. In closing, we are convinced that the use case for ai will benefit investors and the resiliency of the u. S. Market and the other markets that we serve. Thank you for the opportunity to testify. Im happy to answer your questions. Thank you. We will recognize for five minutes for questions. I should mention to the members present. It looks like the latest estimate for votes are now 11 30. So we may in fact have time for a second round of questions for members that are interested. Well have to play that by ear. Doctor, you note in your testimony that data vendors offer a wide range of data sets. I think other witnesses mentioned that. You know, things that were not available a couple years ago. And not only the data itself, but the Processing Power to analyze it and the realtime delivery of that data is becoming more and more important to successfully trade on it. Could you just illuminate for us some of the more Interesting Data sets that you now see being used . Certainly. Its a combination of data sets. On one hand, we have access now to credit card transactions, location data, satellite images, transcriptions from earnings calls, engineering data, data from engineering processes that allows us to assimilate better, also the data. Keep in mind please that 80 of all data recorded today was generated over the past three or four years. Going back to history, there is a lot of data around, data that we arent even aware of, but from websites, all this data can be used to understand what is the psychology of people, what is the state of mind of people, understanding if people are more inclined today to take risks or to, for instance, relocate their assets to fixed income instead of stocks, try to understand from news articles, as one of my colleagues mentioned, what are the narratives associated with companies. So the amount of data today is staggering. This is only going to increase because the search for data is becoming cheaper every day and the Processing Power is increasing. So this is definitely a trend that is not going to stop. Yeah, and now that as i think i mentioned in my opening remarks that that has a danger of driving monopoly, that, you know, the return to scale because you get more correlations to look at with your ai if you have the full range of data, and so this will naturally cause the smaller players in the market to not be as effective, less profitable and ultimately i think thats part of probably what you are seeing in High Frequency trading, the consolidation that you are seeing there. Now, is there any way around this . And how hard should we lean against the natural tendency to monopoly here in, you know, financial trading . So there are two schools of thought with this regard. Number one, there are a number of academics who believe that this consolidation is not necessarily negative in the sense that the few survivors that are able to consolidate for instance High Frequency trading today are operating like utilities. They are not making the kind of returns that they were able to obtain nine or ten years ago. Essentially what happens is they break even. This technology is becoming so expensive that they have to spend the time and money in order to achieve profit that is dwindling. So there is a number of academics who believe that actually consolidation is not necessarily negative. Theres on the other hand of course the problem that those [inaudible] could cause a domino effect in one of those fails to provide liquidity. Theres a need to strike a balance between on one hand preventing too much consolidation, on the other hand, also, favoring competition between the operators. Ms. Wagner, you mentioned that this actually netted or at least electronic trading generally netted out very positively for someones retirement account, that it actually because of the lower, you know, offer spreads and transaction costs, that actually i think you quoted 30 more in your retirement account as a result of this. Correct. So similarly, when ai is widely applied, if it is very effectively deployed in princip principal, we have more efficient Capital Allocation across our country, so is actually the best strategy to let a small number of very dominant players have access to all the data set to get a more efficient economy and or are we better off just letting a thousand i think it is absolutely vital that we encourage policies that promote strong competition in this space, and with High Frequency trading and automated trading, we have seen such fierce competition over the past decade or two, that were approaching near zero latency speed. Were approaching the speed of limits but also more monopoly. My time is out here. This is something i intend to if we get a chance. Im happy to respond. Thank you all. I would like to remind the witnesses to speak as directly into the microphones and as close and as loudly as is comfortable for you. I yield five minutes to the Ranking Member. Thank you, mr. Chairman. Ms. Wagner, as you know, the sec has experienced some cybersecurity difficulties, especially in the 2016 edgar data breach. I think its important for the sec to only obtain proprietary trading algorithms if absolutely necessary, with a subpoena. So i was wondering if you could discuss why it is important for source code to be protected. Sure. Thats a very good question. So the real life blood of automated trading and the kind of secret sauce is the source code. That is a valuable intellectual property that the different firms are competing against each other with, not just domestically but globally. And just like a selfdriving car Company Needs to keep its algos and source code, intellectual property protected from misappropriations so do algorithmic traders rely on Government Protection for their intellectual property. There was a proposal number of years ago to perhaps collect ip source code and put that in a government repository just in case it was needed. That never came to light, but its still something were absolutely educating policymakers on. This should be i think a bipartisan area of interest to ensure that we have a globally competitive marketplace that protects intellectual Property Rights as well. I appreciate that, from my time in the military, working in intelligence, we had a principle we lived by because of the sensitivity of the data that we collected and maintained was if you dont need something, dont keep it, which means if you dont have to protect you dont have to protect what you dont have, and my concern is how vulnerable the industry becomes because quite frankly the government tends to be the weakest link when it comes to Data Security in some aspects. I think obtaining that source code is not only just a violation of the privacy right of the business, the coder, but it could also be National Security risk. I think thats right. If bad actors were able to breach the source code, it would be presenting an opportunity for manipulating the markets or, you know, cyber risk, so it is absolutely vital that we protect intellectual Property Rights as source code. Thank you. Ms. Fender, the adoption of Artificial Intelligence and electronic trading can disrupt the job market and displace for traders but technology also creates a needs for more workers in other fields. Today we have about a Million People working in the airline industry. But in the early 1900s, Washington Post led a headline that said man will never fly and shouldnt, and part of their argument was the displacement of people in the job market. Could you touch on the job fields that are growing because of the use of ai in the Capital Market space . Yes, thank you. As you noted, there are many ways that jobs are changing. Adaptation is really the key. We surveyed industry leaders, the people doing the hiring, and we said what are the most important skills Going Forward that maybe its not necessarily the job description, what are the skills underlying who will succeed in the future . And they talked about something called t shaped skills, right . This is the idea that if you think about the letter of t, you have the vertical bar, where theres deep subject expertise and horizontal bar where you can cut across different discipli s disciplines. The ability to connect is where theres a lot of opportunity. And so these are the innovators. This is an area where you will see more research needing to be done so that we understand what the trends are. And the key thing is that people have to ask the right questions. Firms are realizing you have to think about the are or why of gathering this data. Many will say a large percentage of the data isnt that useful, so you have to be smart about how to do that and start the process with investment professionals. Okay. And so what im getting at is not all the jobs are going to be as deep intellectual being able to code or algorithms and that but there are an ancillary jobs come because of that development. Exactly. They are going to need data scientists on their teams and speak the language and Work Together. I want to talk about the use of Artificial Intelligence and fraud detection. I view cybersecurity as the biggest challenge that we face in this nation, both from a business, government, and personal perfespectivperspectiv. Can you touch real quickly running out of time how al go rit ms are used algorithms are used to detect unusual behavior . We rely on algorithmic coding to pick up on unusual patterns we see. Everything needs to be compared to something that is usual; right . So we program things to pick up on 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. Thank you. At this point, i think well leave that to hopefully your next round of questioning. The gentle woman from North Carolina is recognized for five minutes. Thank you very much to the chair for putting this together. We appreciate it. And also those of you who have come to testify. Thank you for your comments and your work. Automation technologies, which enable the transfer of tasks from human labor to machines affect approximately 6. 4 million workers employed in the Financial Services industry. Specific Industries Like credit lending and Capital Markets are being affected by ai as human task involving Data Analysis, Decision Making and compliance are replaced by Machine Learning robots. The shift in job automation could predict which jobs in Financial Services will be replaced and what new jobs could be created. Ms. Wagner, specifically examining loan underwriting compared the traditional methods of meeting a loan application in person, to what extent does ai replace or augment the work done by loan officers, credit counselors or other credit underwriters . Thats a good question. In the Consumer Lending context, i think it is very important that ai as a tool for humans, when they are extending credit and loans that there are systems in place to ensure there isnt any sort of algorithmic bias, and in my prepared testimony, i noted some suggestions. Our members are not engaged in the Consumer Lending context, but we have our own insight. I think that Loan Companies individually or collectively could employ officers to ensure theres an algorithmic bias in the lending context. I think it is important that industry members share Lessons Learned as they explore how they are using access to credit and finding the most efficient ways to extend that credit. I think it is really vital that we act now to make sure as were bidding out this building out this system that we minimize the risk for algorithmic bias in Consumer Lending. I think it is vital. Thank you, maam. Is the u. S. Properly equipped to remain competitive in the Financial Services workforce . This question is to dr. Lopez and to ms. Fender. The u. S. Is leader in the Financial Services industry today. My concern is that the leadership is being challenged by the fact that on one hand we are not investing as much in ai as other countries. Number two, the fact that we are educating our competitors. In my remarks i mentioned that im very concerned that the innovators of the future are attending today a class in our universities that they will not be allowed to stay, and as a result, yes, we are very competitive, and this competition this ability to train the specific skills is going to turn against us if we arent able to retain this talent. We have seen that it is early days for how this changes our industry with only about 10 actually using these techniques, but what we are seeing is that, you know, firms are doing ai labs. They are doing innovation hubs. They realize this is something they need to be proactive about. Out of our case studies, things in our case studies had to actually be in practice. There is a lot of talk out there, things are actually in practice, here in the u. S. Doctor, are we adequately teaching the skills needed for the jobs of the future . Thank you for the question. I think were adequately teaching those skills. I think the questions about who has access to that teaching, and so when we think about underrepresentation of certain individuals, members of the workforce, who are not getting the types of education that are needed for the jobs that may be coming online as a result of automation and Ai Development, and so i think if were to have a full pipeline of folks that are able to receive what it is that we teach in our colleges, universities, even high schools and younger, then we have to be more proactive about making sure that all people have access to that teaching and that information. No one left behind . Absolutely. All right. Appreciate it. Im going to yield back, mr. Chair. Thank you very much. Thank you. And the gentleman from indiana, mr. Hollingsworth is now recognized for five minutes. I appreciate each of you being here today and appreciate the chairman for holding this hearing. This is an important topic, something i have been really passionate about it, since arriving here in congress, and dr. Lopez, i appreciate your comments because what you have touched on is something that i have been an ardent believer in for a long time, and that is number one, that the big arm of the federal government isnt going to stop the growth of this technology, isnt going to cease the investment in ai, either here or around the world, and while we can shape the context by which that technology flows, we are not going to dam up and stop the technology, so when people say job losses may result on account of this, right . Theres a lot of fear and a lot of desire to put an end to that and stop that, but i like how you referenced a lot of training and retraining that may need to happen, training individuals that are graduating from school to ensure they have the skills that are necessary in a 21st century workplace but also ensuring those that are already in the workplace have the opportunity to get the retraining to continue their competitiveness. As we see further growth and development in ai, it will require more and more frequent retraining to stay ahead of that, to stay relevant in that field, a very competitive field. The second thing you touched on is something im more ardent about, but is we educate a lot of kids in the country. We do Higher Education in this country better than anywhere else in the world. We bring a lot of talent into this country. We invest a lot in those kids and then we politely ask them to leave at the end of their tenure here; right . Thats embarrassing, stupid and i hate that. I want to find a way to attract talent in this country and retain it. Not because i believe it is a zero sum game but because i believe this country can provide Technological Development that you cant find elsewhere in the world. I think that technology will benefit human kind over all the world in the long run. I want to make sure we do that. I appreciate you touching on those topics. I appreciate the investment. Ms. Wagner, i know that you have a source code event today, yesterday, tomorrow . This afternoon. This afternoon. To talk about source code again, and i really appreciate you continuing to educate a lot of people about how important that is, where i go, all the way across the district in indiana, i hear more and more how Much Technology and investment and ip is in things that arent readily seen, in business process, in the source code, in the technology, underpinning automation itself. I know how important that is. I really appreciate you bringing that to light. All that being said, i wanted to ask a question. That is maybe a little bit far afield from what were talking about today. I had some people in my office earlier this week that were very complimentary frankly of nasdaq surveillance services, were very complimentary of they were Public Companies and how when something seems amiss in the markets that the nasdaq was very quick to pick up the phone and Say Something seems amiss, lets figure out whats going on here. One of the things that is very important back home is biotech; right . A lot of biotech firms a lot of people dont know that. Were trying to get the word out of it. They are concerned about market manipulation specifically short selling they promote the idea that there should be disclosure around short selling similar to long positions. They came out and said that disclosure around short selling would help us as a firm better understand those that might have interests adverse to us because we cant really track that right now. But the counterargument that they made was gosh nasdaq seems to be doing a really good job of figures out when theres potential manipulation. I wonder if you might touch on that. Is disclosure in short selling something that would benefit the market, is something that would benefit these firms, or do you feel like you guys have enough of the ability to track potential market manipulation on the back end . Again, im not pejorative against short sellers. I want to make sure it is legitimate action, not market manipulation. I wonder if you might comment on that in the last minute. So i think disclosure is a big part of surveillance; right . It is information thats always needed to understand what is happening. Okay. I do think that what we have today is sufficient. I mean, as you say, we have a lot of patterns that are detecting manipulation such as short selling. Great. Or i might say the troublesome part of short selling. Right. Short selling is legal; right . Right, of course. It is really to detect what is how to be used in an unnormal way or manipulative you feel like you can detect the activity that would be illegal or abnormal or different adequately. The question is, what do we do with it after that point is maybe where we should focus Public Policy attention, is that fair . Yes, and but to be fair also, there are other parts that handles more of the policy questions. Okay. But for me as surveillance practitioner, i think disclosure we have and the tools we have to monitor the markets are adequate . Yes. Great. When they were in my office, i think thats the question where do we need to focus policy attention . Perhaps it is beyond surveillance and focus the penalties or actions that happen with the enforcement agencies. With that i will yield back. Thank you. Im very encouraged that one of the areas of bipartisan agreement here is the insanity of this business of worrying people of their phd diplomas and putting them back on an airplane. Thats one of the reasons i was proud to introduce this session of congress, the act of 2019, designed as a rightful shot to just exactly solve this problem. I really look forward to my colleagues support on this. And now i would like to recognize the gentle woman from texas, ms. Garcia for five minutes thank you, mr. Chairman and thank you again for holding this hearing and thank you to all the witnesses. Good morning. Welcome. Wanted to focus on a couple of issues that some of you have already talked about, and, you know, like ms. Adams, im particularly concerned about jobs. My district is from houston, and 77 latino, and it is also working class. Were always concerned about jobs and im encouraged that you all seem to have the consensus that although there will be some job displacement, that there will be new jobs created. My main concern of course is whether or not we do have the skill sets to transfer those skills or to make sure that we can fill those jobs because in the end, thats what really matters to families in my district. But im also concerned with automation and the difference between, you know, ai and automation and how we can Work Together, specifically in the area of Regulatory Compliance. So ms. Fender, in your experience, has ai and automation affected ip institutions Regulatory Compliance . Is it improving . Is it still a work in progress . How are we doing . Thank you. Thats a very good question, and again, i think it is still kind of early to know; right . We hear so much about what is coming, and yet, you know, so compliance areas are growing in firms; right . Clearly. And now we have more and more data, and regulators are going to be able to have the same sort of data; right . Now, the question is, is there a greater risk of maybe Insider Information now . Right . You collect more data, and people can see lots of different patterns out there, and if they see that and can trade on it before the market, then you have challenges for the sec i think in terms of reg and so forth. Simplify and ensure Regulatory Compliance with the federal agencies in charge of supervising the Capital Markets . Sure, i mean, i think as the data sets become more complex, as you just alluded to, i think it will be vital that the regulators have resources to have their own ai either independently of companies or together with the companies through Public Private partnerships as the bad actors become more sophisticated then were talking about global bad actors, we need a strong cop on the beat here in the u. S. I think it is very important that the private sector Work Together with regulators to ensure they have the resources and Congress Really explores the sec and cftc has the resources they need because, you know, the systems are becoming much more complex and reg tech is evolving but needs to keep up with the pace of that technology. That is a big concern of this committee is those bad actors as youve described them. So how can ai assist us with antiMoney Laundering, suspicious activity reporting . Are we well prepared for that . I know we did a [inaudible] several countries and things are getting more sophisticated. It seems like the bad actors have more money and better things, you know, 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 this. I would say the new head of innovation at fnra has an excellent group. They just established themselves this year. Theyre a fantastic resource. Theyre working together with other regulators, with private sector participants, to gather information about best practices and to really make sure we have the best technology. This is 100 something we need to be focused on. In your opinion, do you think that our regulators in our oversight entities are well prepared in this arena, or what else should we be doing . I think we need to be investing in technology. Theres always room for more technology with the regulatory agencies. I think the sec has been a very positive example of the sec using very Sophisticated Technology to surveil the markets, but i think this is a constantly evolving space as everyone here as noted. Weve got to keep very much on our tiptoes on this and keep on investing in this area. All right. And ms. Fender, did you want to add something . I think the yeah, the more data we have, the more complex it gets; right . And one of the other things that were really concerned about is the Investor Protection side too; right . And if bad data goes into these models, they can be marketed in many different ways. And so disclosures are really important. Understanding your clients, understanding where the money comes from and understanding what clients are really getting all kind of goes together. Thank you. Thank you both of you. I yield back. Thank you. The gentleman from virginia is recognized for five minutes. Thank you, mr. Chairman. I want to thank all the witnesses for being here today. Im so happy that all of you are here. Im not showing any favoritism. I would particularly like to welcome ms. Fender from the Cfa Institute which is located in my district in charlottesville, virginia. It provides a host of resources for professionals who work in the Financial Services industry who are among the most qualified and adhere the highest codes of standard in the financial industry. Im honored to have such a distinguished group preside in the fifth district. Although she isnt a constituent herself, her organization emp y employs many of them. Im thrilled to see you here today. Welcome. Welcome to all of you. I will start with you, ms. Fender, you probably knew that was going to happen. Can you talk about how cfa is adapting the charter to the ai and Machine Learning innovations in the investment industry . Thank you very much. And pleased to be here representing virginia. The Cfa Institute is really the global standard for investment practitioners, right . The people that have our credential are the Portfolio Managers for your 401 k . Theyre the chief Investment Officer at the public pension fund. They are people that are really safeguarding the Financial Futures of so many people, and so its imperative for us to keep up to date on what we teach. So i mentioned earlier in my testimony that weve just added Machine Learning into our curriculum. This is a significant indication that we are seeing the market change, and we need to prepare people. So we have a group called our Practice Analysis team. They are out there all the time going to these conferences, figuring out whats the next thing that people need to know because Global Demand for Investment Management is growing and especially for those who really combine both competence and ethics. There is a reason i asked the question. My prior job, and we talked about monopolization of data, things of that nature, i wanted to monopolize as much as data as i could in terms of critical analysis when i worked there. We had to aggregate that data, consolidate, aggregate, analyze it and execute it, based on Human Behavior. This is the exciting part for me is the technology part. Do you see when we did this, we had multiple data sets that people had never seen before. We talked about the challenges of data. We had multiple data sets. We had data we had never sort of aggregated. We thought we had the right answer and we found out we didnt, Human Behavior analysis. Do you think thats something you will see more of in the future, is that there wont be a human in the loop or more Machine Learning roles to sort of mimic what Human Behavior does with certain role sets . Do you think we will see more of that, taking humans out of the loop, for analysis or fraud or anything of that nature . I think we are a long way from that, at least. I think for now the way we do it is really to have the data that we have, that we analyze. Now were applying a new technique to give us more better overview that is not that [inaudible] driven. I think the human in the loop is the way to go because there is much more analysis that needs to be applied after the output has come. And i think thats one that will be for a while. It is interesting you said that. When we saw the human we thought we could take the human out of the loop in some of our processes and found out it was not a good idea. I see some head nods back there. We tried to do that. Dr. Lopez, you were talking about there could be some advantages to sort of aggregating as much data in one place as we can, right, looking in the gaps of that data. Thats the thing ive been trying to wrap my arms around. My whole job was not competition. It was to monopolize all the data and then use competition to give us the best Algorithmic Solutions we could for first, second, Third Order Effects to what happened to a specific part of the network. This is a tough question. To be this objective in 40 seconds is going to probably be ridiculous, when you are looking at this, do you think i know this is a tough question do you think with all the proprietary technologies out there, do you think there will be a voluntary sharing of that data if we find something thats very good, right, across multiple sets . So for example another company, do you think we will have that type of sharing for proprietary Solutions Based on algorithmic types of analysis . Do you think that would ever happen . Do we have to sort of force that to happen when we monopolize that kind of data . I hope that makes sense. Are you referring to sharing the technologies with private and Public Companies . Yes. When you look at the nasa model, there has been a lot of transfer between the agency and various contractors. So that could be a model that could work for the cftc and the sec. In particular, in my remarks i mentioned the crowd sourcing of investigations. How companies or private participants could establish tournaments to have agencies identify market manipulators. Thank you very much. I yield back the balance of my time. Thank you. The gentleman from illinois is now recognized for five minutes thank you, mr. Chairman. Thank you all so much for coming. I had back in my prior life, i had head of engineering who said every advance in Technology Gives us more precision and less knowledge. He had to know the order of magnitude of his answer and got 16 significant digits and could never remember whether it was millions or billions. Of course in my lifetime we have gone from foldable maps to gps and give me latitude, longitude and i cant tell you whether im northeast north, east or west of where we started. At one point i built a genetic algorithm to predict the revenues of our utility business, and it was amazing. I cut our revenue forecast variance by 90 . I have no idea how it worked. And, you know, thats the power and the frustration. And i mention that because i think most of you have talked about the consumer benefit that comes when we get all these ai algorithms out in the markets and we get lower trading costs, lower bid ask spreads. Thats all terrific. The question i have and a lot of have also talked about bad actors. We can put up monitoring for that, and thats also great. The concern i have is this tension between the transparency of the model and whether the model can actually effectively replicate a bad actor that we dont understand because its fairly easy for me to imagine a trading algorithm that is tracking a bunch of data and has figured out how to bet on one country and making money. I can imagine a trading algorithm thats looking at changes in current si flows for illegal activity that not itself is illegal but arbitraging spread from results of that. I wonder if you could comment that tension between transparency and algorithm and robustness and to what degree we have or need regulatory tools to stipulate where we sit on that continuum sure. I mean, i think transparency is absolutely, absolutely vital. I think its also very vital that regulators and the exchanges have the resources that if they note any sort of irregularity in the markets, that they can immediately identify that, and to the question of, you know, whether or not one needs to get source code, if there is a detection of some sort of illegal or irregular activity, that then the regulator requests but if i can just clarify, first, would you agree that the more transparent the algorithm, potentially the less powerful the algorithm . I think to the extent that the algorithm is not subject to intellectual Property Rights, that transparency is absolutely vital, if were talking about intellectual Property Rights in this source code of the algorithm. Thats proprietary information im not referring to whether or not the public has access to the algorithm. Im referring to whether or not our human brains can understand how the algorithm works. Got it. I could give you the genetic algorithm i wrote. You couldnt understand what its doing. That question becomes more complicated in the Machine Learning context especially you point to an interesting question, right, as the commands become selfacting in a way, that they are basing their analysis on the existing data sets, i dont think were totally there yet, but i think thats something we definitely need to explore. What is our policy . Thats an interesting balance. Question, and this is for you, but for really all the panel, i think thinking about that problem before it gets there because it strikes me that there will be a pressure for every trading firm to develop the most powerful algorithms which by definition at some level are going to be the ones we have the least ability to unpack and understand. I think this is an important question that the industry could get together on and share their best practices how do you balance that . Anybody who thinks they have got a great answer on this. Number one, how should we do that . Number two, to what degree do we need to coordinate internationally because even if we do everything in our country, since all these markets are so interlinked, is this a u. S. Problem, or is this an International Problem . Anybody have thoughts on that . If i may, i think we have these are very important distinctions. Black boxes tend to be less reliable than transparent solutions, particularly in finance because we are dealing with problems where the signal to noise ratio is very low. In astrophysics research, why is the signal to noise ratio is low is because of competition, because of arbitrage, otherwise everybody would be able to extract profits from the market. When we deploy black Box Solutions in finance, these solutions can identify patterns that are not real, that are just patterns in the noise, leading to Investment Strategies that will fail. So one solution would be for an investor to understand very carefully when a product is based on a black box solution as opposed to a transparent Machine Learning solution. Thank you. I yield back. I would welcome any of your comments if you have follow up in writing, please share. Thank you. As i mentioned, were likely to have another round for members that are interested here. The gentleman from missouri who is also the chair of the subcommittee on National Security and International Development and Monetary Policy is recognized for five minutes. Thank you, mr. Chairman, and i really appreciate you calling this hearing and appreciate all of you giving us your time. I dont know how were going to deal with ai and human beings. Long before we had flip phones, captain kirk had one. And long before we had the smart watches, mr. Spock had one. And a lot of attention is always paid to hollywood, particularly in science fiction, and the military, our own military, and so a lot of people have their eyes on a fearful future as it relates to ai. To be straight, im one of those. You know, im conflicted. I know we cant hold back the wind, and it is inevitable that were going to see more and more of this in the future, and im not sure that we ought to we ought to try to hold it back, but to the degree that we can control it, thats what i think we ought to do, and im thats where im concentrating most of my interest. Doctor, first of all, thank you for being here. But im wondering how inclusive this new technology is right now, and what can we do to make sure that in the future, that every component of our great mosaic in the United States is a part of it . Thank you for that question. Id share a little bit of your fear because what we know persists as technology changes, as technological advances are made, is that some people and typically the same groups of people are left out, left behind, are disadvantaged, and so 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 in this panel have at least alluded and nodded to, that is, when we look at our Technology Sector, those who are prepared to be part of that sector, those who are currently working, building the technology as 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 inequalities and devastations that technologies, ai and automation included have been because not everyone has been included in the decisionmaking about what technologies will be built, why, for what purposes, who they will advantage and disadvantage. So i think moving forward, we have to change that, that is, we have to invest strategically in building a more inclusive workforce, in these sectors that are growing, that is, the Technology Sector, in the Financial Sector as well. how should we direct young people right now. Who are scientifically gifted. What should we do. Responsible innovation. I know our members support getting out there. Middle School Students geographically. A diverse opportunity for kids. And to partner with different parts of this goals and country. Just recruit now. Just get interested in these fields from a young edge. We just have to get there early. We need to make sure that we give good brawl models. Thank you very much. Not just have time for a brief second round of questions. To make had sort of two different narratives. One is the optimistic narrative. The machine skills orman human intelligence. There be an uncommitted human intelligence. Then we also have mention of intermediate way of transfer lying. You can actually use one field of expertise and transfer what was learned there to another field. Thereby replacing multiple human missing pairings. One example of that was the example from the jesus who are using satellite data to analyze symmetric analyzing satellite imagery. To predict things. Then potentially using the transfer learning so the knowledge can be transferred to Copper Mining or whatever. On the other hand, there is no alternative narrative they disaggregate all of the data you can. And you see that i want it general purpose learning trading algorithms to look at all satellite data and look for all market correlations. That would detect not only the simmons market, it would also look at the parking lots at toys r us to predict that theyre going bankrupt because they didnt have many cars on black friday. So this sort of think could be written once and deployed to replace tens of thousands of event machine human pairings. And obviously with much Smaller Labor input and need for a few months which of these two narratives are going to end up winning and how is it going to net out for human participation in this. I can start. Foundational concept. Correlation has brought necessarily causation. We have a lot of data, we see these patterns be need it human to ask what is the right question. I mentioned also the example of going through the news story bloomberg. They said the key question there was to go through these articles in a state what we think the author of this article wanted to get across but when we think people are hearing. There are a lot of nuances really about houses going to play out in the swipe again, and the collective intelligence, and adverse news perspective will be imported. I think that the two narratives have some part of truth. I think the shortterm, we we have reasons to be worried in terms of the transfer of knowledge and the potential that they will be more broadly deployed but i think in the long term, i can be optimistic. Because the next generation will be better report prepared. It is very important that we keep access to education equal access to education bridge is very important that we encourage kids to learn how to program and participate in math. And be flexible. A workforce that in the future knows that what these technologies will do 20 years. Will you be able to engage proactively. News are a danger that is going to squeeze all the profitability out. If you had complete knowledge of everything, very efficient algorithms immediately trading on the knowledge, the 30 percent improvement in your retirement savings, all of the money used to end up in the pockets of people with oyster base. And that is sort of the nature of things. It may be that when we get this more efficient economy with extensive deployment of ai, just the toll amount of money left to be extracted will continue to go down the same way hyper trading is sort of suffering. If i may, one view in fact having a perfect market has brought necessarily bad. The data could go to our Financial Advisor and we think and receive the same treatment as when we go to the dr. The article. In order to achieve your retirement goals. I think that is a good outcome. Are deficiencies in global advisories and more efficient i would see, house and Asset Managers will be able to deploy that. I think it also raises a global competition question because will not talking just about domestically will talking internationally and will not going to time all across the world. Other countries are innovating ai. It is inevitable that we will be competing in that space. We want to keep the u. S. Market the envy of the world. So the future Financial Advising his conversations with alexa, i guess it comes down to the objective function that the ai running alexa is maximizing. Is amazon news profit or is it linear combination of amazon news profit and diversity inclusion, a secure retirement rather than stirring people in the product are profitable for amazon. Think my part here is that as you mentioned we have competition bread that theres not is it too much aggregation of power in one entity. We need to have a policy that is the robust competition save the robo advisors make sure there is data accessible competitive prices so there has brought a brat to entry. An exciting space where Financial Services and Judiciary Committee on anti trust, will all became more technology and technology more finance. These are the right questions. Thank you and i yield. I like to go back and continue our consummate conversation that we will talking about cybersecurity and using ai and fraud and it wasnt very well at managing that time before. Could you explain further how nasdaq is actually using ai and fraud detection. I think it is important to start that the future is here. We have billions of data points. Massive amounts of data that needs to be analyzed to capture anything that is fraudulent or that the market. So we have an environment already. We have been doing so far is deploying ill read the make coding to sort out and be able to process all of this data very fast. Realtime surveillance is picking up on unusual behavior within seconds after it is happening in the market. So there is really a fast and efficient way to go through the data. But as it is growing exponentially growing, in need of course to continue to invest in other ways of looking at somewhere ai, its more and broader approach, and it doesnt have to be specific but we are today so we can capture more things that is more sophisticated. As we have been talking about, subtly us, using this technique for the participants in the market are using it as well. Something is important for us to match the technology with our technology. Thank you. Doctor lopez, can you touch on the differences between automated trading Algorithmic Trading highfrequency and computer training. How think not the same and what differentiates those. Yes. Trading fall is a set of rules. When the computer fall is rules. It does not require much in learning. Machine learning, is the learning of patterns. It set of data, with out directing that learning. Essentially what happens is it gets in aloe algorithm, and the data set identifies a pattern that will not aware of. That is machine burning. The automated trading in High Frequency. A with High Frequency training, it will happen with or that went out Machine Learning. And there is that you are talking about, trading will correlate with intelligence but today is what you see as many providers Market Makers hedge funds, deployed solutions with Machine Learning invented. Ms. Wagner, who had some discussion on the cost savings that have resulted from ai and automation and Capital Markets. Do you see that these deficiencies are significant reason behind the record returns of investors have enjoyed in the last decade. A definitely contributed to the returns. Every reduced instrumental cost compositing interest over time. So if the market become more efficient, investors are going at more in the pocketbooks. Weather they have of american it is in 529 plan or otherwise. Positive. No further questions. I yield back. Thank you mr. Chairman. I am interested in how do we do planning now. For example we just updated our anti Money Laundering, the bank secrecy act. Im sitting here now, and introduced, ive been feeling pretty good about myself until you guys came up. I am thinking why do we go through all of that. That dies are trying to figure out how they can exploit whatever we done legislatively. Howdy cai, involved in anti Money Laundering efforts like the legislation that we hope it will take up during our lifetime. If there is any way that you think that that can play a role that they can play a role, in our monday Money Laundering deals and help we have turned it or reduced it. Never probably going to eliminate it. This is the problem that we have to tackle tremendous amounts of data. And identify this needle in a haystack. I think in a practical solution is to for regulators to Work Together for the scientists. The entire community. We need to learn this data into the community is on the community can be rewarded with enforcement. I guess the wrongdoers but i think that is very doable approach even number one, how difficult it wouldbe for the agencies to develop the techniques that the wrongdoers are developing. Number two, the amounts of data that we need to pass through. Our committee yesterday and of course this issue, the agents, investigatory part of the treasury. Im here wondering what they are doing trying to keep up with the technology and what challenges they face or going to face in the future. And so, you guys have destroyed all of us every think i was proud of. But we appreciate you coming here anyway. Thank you very kindly. Do you live. Would like to witnesses for their testimony today. Everybody has five days to forward questions. All members will have five legislative days with which to submit extraneous materials to the chair for inclusion in the market for nick and hearing is now adjourned. [background sounds] [background sounds] this holiday week, booktv is on cspan2 every day. With hunting features each night. Starting tonight, any 30 00 p. M. Eastern with nikki haley, and about with all due respect. To stay and 8 00 p. M. Eastern, Supreme Court justice, and his book a republic if you can keep it. Wednesday at 8 30 p. M. Eastern, in depth, with the amber klein. Her latest book is on fire. Thursday at 8 00 p. M. Eastern, Nancy Eisenberg and andrew persing discussed president s in the books, the problem with democracy. Friday at 8 00 p. M. Eastern, pharaoh and his book, catch and kill. Watch a special airing of book tv, this holiday week. And every weekend on cspan. Bass will be in order. For 40 years cspan has been providing america unfiltered coverage of congress. The white house, the Supreme Court, and corporate policy events from washington dc and around the country. You can make up your mind. Rated by cable in 1979, cspan has brought to you bag of local cable or satellite provider. She bent, her unfiltered view of government. Christopher ford assistant secretary of state for interNational Security into nonproliferation. Talks about Nuclear Security policy. Nuclear terrorism prevention, security north korea in the surrounding region. Ladies and gentlemen very good afternoon to all of you and welcome to the center. And the president ceo here. Those of you are attending this very event for the second time. Many apologies. It was not as you may have assumed our speakers agenda. That