Transcripts For CSPAN3 Hearing On Impact Of Artificial Intelligence On Capital Markets 20240713

At any time and without objections, members not on the task force are allowed to participate consistent with the committees practice. And this is entitled robots and ai and the impact of Artificial Intelligence on the Financial Service industry. Thank you all for joining us for what is an interesting hearing of the task force. Today, we are looking at exploring how a. I. Is being deployed in the Capital Markets from automated trading to port follow allocation to Investment Management decisions. We are also 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 skillset needed to service others. It is not an exaggeration today to say that wall street is quite literally run by computers and long gone are the days when the traders would be screaming on the floors of the new york stock exchange, and they would use t. I. Calculators and pour over the ticker tape to determine the companys value. I hear about the days from the limo driver who takes me back who was a floor trader on the merc. Today, the trades are calculated in milliseconds, and they rely on algorithmic models to determine what benchmark it is tracking. And others have benchmarks to scour the data to find the stocks with the highest momentum or the highest dividends or looking for the correlations that will be in the market or the external data feeds to provide the most value for investors. It is notable that a lot of the shakeout that we are seeing in the markets is really a reflection of sort of the winnertakeall of the digital economies that any Digital Business, surely Digital Business is a monopoly and as more finance is digitized, you will see the rewards going to the smaller and smaller number of dominant players, and i would like to emphasize it is not meaning that they are evil, but it is a natural reflection of the digital marketplace. And other managers may use the algorithms to calculate difficult algorithms in real time. This is satellite information and web traffic and anything else that you can think of. This is i guess good in terms of having the market reflect all known data, but there are abusive corners and imagine what it would be worth if you had a 10second early look at trumps twitter feed and how much money you could make off of that for example. The three types of computer managed funds, and the etfs and the quan funds make up 35 of the 1 trillion public equities market. And other managers of mutual funds and other funds manage 24 of the market. The rise of the socall computerization of our markets has a number of benefits. The cost of executing the trades has gone down and sometimes to zero dollars and more liquidity in the markets. The assets are passed each year while others charge 20 times that much in asset managers. It creates questions as with the 2010 flash crash and the more recently mini flash crashes have shown that algorithm trading can have unpredictable consequences to have market volatility, and to have other symmetry with other types of firms, and to have fast tracked data sets to obtain a competitive edge. And also, how these are impacting the nature of jobs in the financial industry. And Wells Fargo Research report said that technological efficiencies would result in 200,000 job cuts in the next decade in the u. S. Banking industry. While these cuts will affect the back office, call center and Customer Service positions, the pain is widespread, and many of the Front Office Workers traders and financial analysts could see the head count drop by onethird according to mckenzie and Company Report released earlier in the year. The report found that 40 of the existing jobs of the Financial Firms could be automated with current technology. To the first order if you spend your whole day staring at a screen and receiving a large paycheck, your job is at risk. Understanding the skills that are needed to excel in the Financial Services industry of tomorrow, and how we can encourage these skills is one of issues that we must tackle head on and tackle early. In a world where many functions can be done by the automated a. I. Models, what role is that going to leave for humans . I am very much looking forward to hearing from the witnesses on these issues. I would like to recognize the Ranking Member of the task force for five minutes. Thank you, mr. Chairman, and i want to thank each of the Witnesses Today to take time 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 the major networks, but it is something that is very important, has an impact on our lives positively, but potentially negatively and important to be looking into this. As you know, today, the task force is going to examine the intersection between technology and the Capital Markets, and in recent years, there are many Technological Developments including the adoption of Artificial Intelligence and automation that have redefined and reshaped the trading and investing, and the first trades were made in the late 1700s using a manual and labor intensive process. For years the buyers and sellers communicated, and today, the trading and investing is done on the digital platforms, and that i can trade the securities from virtually anywhere in the world using modern technology. Electronic technology has benefitted the markets in many ways and leading to overhead and transaction costs which is contributing to the record returns over the last decade. Several asset firms offer zero Percent Commission which means that the investors can buy and sell stocks for free, and capture more of the growth of the investments. This is not possible without electronic trading and Digital Trading plat forms provide investors with access to low cost, Financial Research and advice 24 hours a day using robo advisers. And the electronic trading makes the markets more efficient by allowing faster searches for the pricing, and larger sets of data and more transparent automation. The proliferation can foster more competition and improve Risk Management and increase the Market Access for investors. In addition to the core benefits, there are many other cases of Companies Using ai to improve the efficiencies in the Capital Markets in unique ways. For example, some Clearing Companies are using ai to optimize the settlement of trades in enhanced cybersecurity and fraud detection. And some selfregulatory organizations are also using ai in red tech and market surveillance. While there are many benefits to electronic trading, kit can als present new challenges. One challenge which is at the forefront of the discussion is the disruption of job market. While the automation of trading has replaced many floor traders, Job Opportunities and fields like code writing, cloud management, telecommunications and fiber optics and Data Analysis are growing. There is some concern that the High Frequency trading can contribute to volatility, but new evidence suggests that the High Frequency trading does not increase the volatility and it can improve liquidity, and some firms dont have the latest technology, and firms that dont have the latest technology could be competed out of the markets. It is important to keep in mind that not all types of the electronic trading are the same, and i am looking forward to the witnesses of differences of automated trading and algorit Algorithmic Trading and other trading. So i look forward to exploring the regulatory issues in this space, and one issue that needs to be protected is the source code, because source code is the core intellectual property and must be protected. We passed a bill out of this committee and the house on a bipartisan basis last congress to ensure that the Security Exchange Commission Issues a subpoena before obtaining the algorithms rather than getting them through routine exams. So mr. Chairman, i hope that we can Work Together on a bill this congress, and i thank you and yield back. Thank you. Today, we are welcoming the testimony of dr. Charlton mcelwain the vice provost for p professor at nyu, and also, we have a professor from Cornell University and ms. Rebecca fender, future director of finance at chartered institution. Ms. Kirsten wegner. Ms. Martin rsial. And without objection, your full written statement will be part of the record. Dr. Mcelwain, you are recognized for five minutes to give an oral presentation of your testimony. Chairman foster and vice chair loudermilk, thank you for this hearing. I will focus on algorithmic bias. We have ample reason to be concerned about the automation of the Financial Services sector. First the Financial Services sector is ripe for algorithm and automation driven sectors. And on the rise, the Third Largest number of workers will be displaced in the Financial Sector even if automation and ai is allowing new types of jobs to be created f. This is true, the cause for concern is clear, and lies with the fact that africanamericans and latin x workers are vastly underrepresented in the Financial Sector workforce. Africanamericans and hispanics and asians make up 22 of the Financial Service industry workforce. And africanamerican representation in the Financial Services sector at entry level and senior level jobs declined from 2007 to 2015. Less than 3. 5 of all Financial Planners in the u. S. Are black or latin x. Africanamericans make up 4. 4 and the hispanics 2. 9 of the securities subsector and asians make up 2. 8 of the central banking and insurance sectors and my point is simple, racial groups that are extremely underrepresented in the Financial Services industry will be most at risk in terms the of automation, and the escalation of fintech development. This is true given the vastly underrepresentation of latin x and africanamericans in the technology workforce. And if we see that it is proportionally and negatively affecting those underrepresented in the Service Industry, we must plan ahead long into the future rather than allowing the market to run its course towards the predictable outcomes. Now, to the subject of deterring algorithmic bias and one way to mitigate the algorithmic bias is to deploy algorithmic systems to regulate governmental and nongovernmental bodies to assess how the systems are used and the outcomes they produce. This is including Technical Solutions that make the algorithms more transparent and automatable. And also before they gain widespread use rare this than trying to simply correct the effects once the damage is done. I wanted to emphasize that especially mitigating the potential disparate outcomes that the algorithms could have on the communities of color, the simple reliance of the technical fixes is not a complete solution. I wanted to end by drawing on the wisdom of a former civil rights leader who had a sophisticated understanding of the automation, and the algorithmic systems that existed in his time. He said today, the unskilled and the underskilled worker is the victim, but the once proud white collar workers began to sink into the alienated world of the american underclass, and as the new poor is meeting the old poor, we find that automation is a curse, but 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 in the fintech era and there they sought to determine if an algorithmic system could reduce discrimination in the traditional mortgage lending than facetoface, and the findings were mixed. Yes, the algorithm mick was mic still discriminating against africanamerican and latin x applicants. And it did discriminate also in terms of price. One of the key conclusions of the study is fintech and other lenders can have issuance of pricing strategies, and we are just scratching the area of data use of algorithmic use. And so this may reduce facetoface discrimination of lending, but algorithmic lending is not alone going to reduce discrimination. And even with the system, Racial Discrimination persists. Thank you, and again for allowing me the opportunity to contribute to these proceedings. Thank you. And dr. Lopez loprada, you are invited to give your opening mark. Good afternoon. It is an honor to be asked to testify in front of this committee today. In regard to big data, the algorithms can perform tasks that until recently only expert humans could accomplish, and an area of particular interest is the area of managed investments. For example, the most investments in history were algorithm, and the key to algorithmic funds is that they are objective and reproducible and can be reproduced over time. And the automation is going to economize to scale and cost reduction. And the order of execution, and forecasting and Credit Rating and fraud detection. Currently, there is a detection of the 6 Million People involved in the insurance and financial industry, and millions will lose their jobs not because they have been replaced by machines but because they have not been trained to work alongside algorithms. Not everything is bad news. Skills become more important in finance than personal connections or privilege of bringing the wedge gap between genders, ethnicities, and other classifications should narrow. It could be a great equalizer. Retraining our existing work force is of critical importance. However, it is not enough. We must make sure that the talent that american universities help contribute and develop remains in our country. The founders of the next google, amazon, or apple are this very morning attending a math class at one of our universities. Unlike the past, these students are in the country on a student visa and they will have a very hard time remaining in the United States unless we help them. Unless we help them, they will return to the countries of origin with fellow students to compete against us. I would like to draw your attention to the rad tech. The first embodiment is the crowd sourcing of investigations. One of the tasks faced by regulators is to identify market manipulators among oceans of data. This is literally a very challenging task like searching for a needle any ahay stack. The practical approach is for regulators to enroll the Data Science Community following if competitions or the netflix price. Accordingly, regulators could anonymize data to scientists who will be rewarded against wrong doers. Next time the Financial Markets experience Something Like the flash crash, this could lead to faster identification of market manipulators. A second embodiment is investment of false products. Filled with false investment studies as a consequence. Financial firms offer online tools and even large hedge funds fall for this trap leading to investor losses. One solution is to require Financial Firms to record all the practice involved in the development of the problem. With this information, could compute the probability that tin vestment strategy is overfit and the probability could be reported in the material. Finally i would like to conclude my remarks with a discussion of bias. Yes, Machine Learning algorithms can compute human biases. The good news is we have a better chance of detecting biases in algorithms and measure that with greater accuracy than on humans. The reason is we can subject algorithms to a batch of randomized control experiments and recalibrate those algorithms to perform as intended. Algorithms can assist human Decision Makers by providing a recommendation that humans with override. Thats exposing biases in humans. Algorithmic bias becomes more prevalent, congress can play a major role in helping get the opportunities. I look forward to your questions. Thank you. Ms. Fender youre recognized for five minutes. Chairman foster, Ranking Member loudermilk, and members of the task force, thank you for allowing me to testify here today. My name is rebecca fender. Cfa institute is the largest Nonprofit Association of investment professionals in the world with 170,000 cfa charter holders in 76 countries. Cfa institute is best known for its chartered financial analyst designation, the cfa charter, which is a rigorous three part graduate level exam. Chart charter holders must have four years of experience. Cfa is a nonpart zan organization and seeks to be a voice for Investor 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 perform today will be substantially different in five to ten years time. Another 5 do not think their role will exist by then. One of the catalysts is technology. Cfa institute sees the impact of technology on jobs in the investment industry as a pyramid. At the foundation we have basic applications. Everyone will need to learn to do things differently and must be more comfortable using and understanding technology. Some people will face tech substitution, but many more will have their roles adapted. In the middle there are specialist apply cautions where technology will enhance work and at the top there are hyperspecialist roles that will be valuable. Cfa institute believes the key to this evolution is ongoing learning. Our exam curriculum includes material about Machine Learning. And among the members and candidates we surveyed in our recent report, 58 have interest in data anal cylists. 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 the middle and top levels of the hierarchy, Investment Management and Technology Teams Work Together. Ai techniques can augment investments to free professionals from routine tasks and enable smarter Decision Making. Investment professionals will spend less time finding and organizing data and more time making sure models are con since tent with how markets work. Ai unlocks unstructured data and can identify patterns more efficiently than humans. Ai can amplify an investment teams performance n. A recent paper ai pioneers Investment Management, weve identified three types of ai in big data applications that are emerging in Investment Management. They are first the use of Machine Learning techniques to improve algorithms used in 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 and Machine Learning techniques in their investment processes. However, here are a few examples from our case studies of what the ai pioneers are doing. First, Goldman Sachs Research Team is better able to analyze National Concrete companies supplying the Construction Industry by using Geospatial Data of 9,000 u. S. Quarries that act as u. S. Businesses. Second, the Data Science Team 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, on few skags, and blame are being used. Bloomberg has had a Sentiment Analysis Product Available since 2009 which analyzing the potential effect of news stories on valuations. They process 2,000 documents a day through their platform. This was only used by hedge funds at first but now many of their clients use it. As the investment community, the speed of volume prevents a new surveillance challenge. Regulators need to have the tools and resources to keep pace with changes. Thank you for the opportunity to testify today. And i look forward to your questions. Thank you. Ms. Wegner, youre recognized for five minutes. Thank you chair foster, Ranking Member loudermilk, and members of the task force its an honor to discuss our future of work force. Im kirsten wegner, chief officer of modern markets. Were an Advocacy Organization comprised of operating trading firms. We together employee over 1,600 people. Our Advisory Board which is half women promotes responsible innovation including advancing a diverse work force in our industry. Over the past decades weve seen automating trading leading to much of the replacement of the Exchange Floor based intermediaries you see in 1980s wall street movies. Technology has reduced the cost of trading by the average investor by more than half over the past decade both in direct trading costs and tighter bidding. If youre an investor in a saving plan, a pension plan, or 401k, you benefitted from todays low cost trading and all the dependable liquidity weve seen in the markets. Studies have shown over lifetime savings, investors have 30 more in their Bank Accounts as a result of that automation. 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 its going to confirm more efficiencies and cost savings for u. S. Investors across the board. Competition in the markets have resulted in near zero trading from fidelity, charles schwab, and robin hood. And weve seen a rise 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. It includes monitoring, reporting, and compliance and processing of regulatory filings, loan origination processing, detection of illegal and irregular trading, and detection of cyber risks. Firms can play a role in working with regulator to share the limited resources in ai and share cutting edge technology. Since 2017, several members have welcomed the opportunity to Work Together with finra in Public Private partnerships, deploying Artificial Intelligence together to surveil the markets. So, automatic trading firms are incentivized because we can be victims of fraud. Its absolutely vital that financial regulators have the Funding Resources so they too 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 complex data that humans alone cannot digest. I think were going to see policies arise from this. I think it was noted competitions and antitrust in the digital marketplace. Were going to see increasing discussion of ownership rights of the data and questions of access to the data and cost of the data. I think alternative data has been successful in helping establish a credit history. Thats one positive. I think we need to continue discussion surrounding algorithmic bias. Ive noted next steps including industry led initiatives to share best practices, utilize ethics officers and reg tech approaches. Last i want to talk about future of work force. 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 fiberoptics industry. Theyre all going to be fuelling the ai economy. Theres massive demand existing for qualified technological talent across all sectors of our economy particularly in the Financial Sector. The current baseline participation for women and particularly women of color is something that leaves room for substantial improvement and thats something were focused on. In a skilled work force for tomorrows work force is only as good as the technologies there to invest in technology. I thank you for your time and yield the time for the next witness. Thank you. Youre recognized for five minutes to give an oral presentation of your testimony. So, my microphone is not working. The button. The button. The button doesnt work. Oh, the button is not the microphone is very directional. Thank you chairman foster and Ranking Member loudermilk for the opportunity to testify on ai in Capital Markets. Many people associate ai with hightech in movies such as the matrix and the terminator but we can target a whole different prey, the fraud ster. We operate markets around the world to protect participants and investors. We operate around the globe and sell Marketplace Technology to hundreds of the worlds markets, regulators, exchanges, and broken dealers. Our department is monitoring the markets for Insider Trading for manipulation as well as handling e real time events in the market. The accessibility in the markets and the increase in players with the ability to deploy manipulative and data quantities can act as the perfect ecosystem for market manipulators to hide amongst the noise. This increased complexity presents new challenges for the Surveillance Team relying on preconceived parameters and known factors to detect manipulative patterns. Our program is using algorithmic coding utilizing over 35,000 parameters. In addition to real time surveillance, there are over 150 patterns covering surveillance identifying wider range of potential misconduct. The team proactively develops tools and meets changing demands in the market. With the manner in which patterns are currently reck nazed relying on unknown 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 e peckations about what patterns look like, calibration also presents a continued challenge when determining the best balance between false positive and true alerts. These challenges led to collaboration between the Machine Intelligence lab, Nasdaq Market business, and nasdaq Surveillance Team to enhance surveillance 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, that a scheme to defraud Market Participants has a pattern to it. An action is taken and the trading is back to norm. So this signaling concept leads to new ways to look at pattern detection. Detection models are not tied to static logic or parameters. We are able to train the ai machine based on visual patterns of manipulation and we started to look at the spoofing pattern. The machine was with human input and transfer learning was used to transfer the learning beyond spoofing. Transfer learning applied a model developed for a specific task as a starting point for a model on the second task. By using deep learning, 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 in in depth investigations. To be clear, the human input is still of critical importance both in analyzing the output from the Surveillance System but also in continuously training the machine to produce more and more accurate outputs. The massive growth in market data is a significant challenge for surveillance professionals. Billions of messages pass through on an active day. Marked abuse attempts have become more sophisticated putting more pressure on the team to find the needle in the hay stack. We are broadening our view of market activity to safeguard the integrity of the Financial Markets. Surveillance is a critical use case for ai but nasdaq is also looking to apply it in other business. For example, were using a version of ai, natural language processing in the listings business to facilitate the compliance review of filings. In closing, we are convinced that this use case for ai will benefit the investors and the use market. Thank you for the opportunity to testify and im happy to answer your questions. Thank you. And i will now recognize myself for five minutes for questions. I should also 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. Dr. Lopez de prado, you noted in your testimony that data finders offer wide range of data sets. Other witnesses mentioned that. Things that are not available a couple of years ago. And not only the data itself but the Processing Power to analyze it and the real time delivery of that data is becoming more and more important to successfully trade on it. Could you just illuminate for us what 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 to credit card transactions, geolocation data, satellite images, transcriptions from earning calls, engineering data, data from engineering processes like Production Companies that allow us to estimate better, all sorts of data. Keep in mind please that 80 of all data recorded today was generated over the past three or four years. Going back through history, going bah to mesopotamia. There is a lot of data around. Data that we arent even aware of just being straight from websites, chats. All this data b ccan be used to understand what is the psychology of people, the state of mind of people, understanding people are more inclined 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 associates with particular companies. So, the demand of data today is staggering. And this is only going to increase because the storage of data is becoming cheaper every day and the Processing Power is increasing. So, this is definitely a trend that is not going to stop. And, now as i think i mentioned in my opening remarks, that has a danger of driving monopoly, that the returns to scale because you get more correlations to look at with your ai if you have the full range of data. 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 what youre seeing in High Frequency trading, the consolidation that youre seeing there. Is there any way around this . And should we how hard should we lean against the natural tendency to monopoly here in 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 today are operating utilities. They are not making the kind of returns that they were able to obtain nine years or ten years ago. Essentially what happens is they break even. These technologies are becoming so expansive that they have to spend this time and money in order to achieve a profit that is dwindling. So, there is a number of academics who believe that actually consolidation is not necessary. On the other hand of course the problem that those small number of operators could have on the market and also could cause a domino effect if one of them fails to provide liquidity. So, there is a need to strike a balance between on one hand preventing too much consolidation, on the other hand also favoring competition between these operators. Ms. Wegner, you mentioned that this actually netted out or at least electronically trading netted out very positively for someones retirement account that because of the lower it offers spreads and transaction costs that i think you quoted 30 more in your retirement account as a result of this. So, similarly, when ai is widely deployed, if its very effectively deployed in principle, we get a 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 . Or are we better off just letting a thousand i think its absolutely vital that we encourage policies that promote trading in the space. Weve seen such fierce competition over the past decade or two that were approaching near zero latency speed. Were approaches the speed of limits i think my time is out here, but this is something i intend to return to. And im happy to respond. Thank you. I would like to remind all the witnesses to speak as directly into the microphones as close and as loudly as comfortable for you. I yield five minutes to the Ranking Member. Thank you, mr. Chairman. Ms. Wegner, as you know, the s. E. C. Has experienced Cyber Security difficulties. I think its important for the s. E. C. To obtain proprietary trading algorithms if absolutely necessary with a subpoena. So, i was wondering if you could discuss why its important for source code to be protected. Sure. Thats sri ar good question. So, the real life blood of automated trading and the kind of secret sauce is the source code, that is the 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 protected, so do traders rely. There was a proposal a 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 policy makers on. This should be i think a bipartisan area of interest to ensure we have a globally competitive marketplace that protects intellectual Property Rights. 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 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. So, i think obtaining that source code is not only just a violation of the privacy right of the business, the coder, but could also be a National Security risk. Is that 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, its absolutely vital that we protect intellectual Property Rights of source code. Thank you. Ms. Fender, the adoption of electronic intelligence can disrupt the job market but technologies also create a need for more workers in more 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 men will never fly and shouldnt. 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. So, we surveyed industry leaders, the people that are doing the hiring. We said what are the most important skills Going Forward . Maybe its not necessarily the job description. What are the skills underlying who will succeed in the future . They talked about something called tshaped skills. This is the idea that if you think about the letter t, you get the vertical bar where theres deep subject matter expertise and a horizontal bar where you can cut across different disciplines. If you think about fin tech, if theres fin over here and tech over here and they arent talking, the ability to connect the two is where theres a lot of opportunity. These are the innovators. This is an area where youll 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 roi of gathering this data. And many of the Machine Learning people 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. So, getting at is not all the jobs are going to be just as deep intellectual being able to code and understand algorithms and that, but there are inciliary jobs that come about because of the development. Is that a fair statement . Definitely. We dont think all charter holders need to become programmers but they need data scientists on their teams. They need to speak the language and Work Together. Okay. Ms. Rejsjo, i want to talk about Artificial Intelligence. Cyber security is the biggest challenge we face in this nation both from a business, government, and personal perspective. Can you touch real quickly, running out of time how algorithms are used to detect unusual market behavior . Yes, as i said, we rely on algorithmic coding to pick up on unusual patterns we see. Everything needs to become paired 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. The new thing here is at this point i think well leave that to your hopefully next round of questioning. The gentlemanwoman from north carolina, ms. Adams, is reck fli recognized for five minutes. Thank you very much to the chair for putting this together. And also for those of you who have come to testify, thank you for your comments and your work. Automation technologies which allow the transfer of tasks from human labor to machines affect 6. 4 million workers in the services industries, specific Industries Like credit lending and Capital Markets are being affected by ai as human tasks involving Data Analysis, Decision Making, and compliance are replaced by Machine Learning robots. The shift in job automation could predict which jobs and Financial Services will be replaced and what new jobs could be created. Ms. Wegner, specifically examining loan underwriting compared to 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 very good question. So, in the Consumer Lending context, i think its very important that ai is the tool for humans when theyre extending credit and extending loans that are there are systems in place to ensure that there isnt any sort of algorithmic bias. 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 could employ ethics officers to ensure there is an algorithmic bias in the lending context. I think its important that industry members share Lessons Learned as they explore how theyre democratizing access to credit and finding the most efficient ways to extend that credit. I think its vital to act now that we minimize the risks for algorithmic bias and Consumer Lending. I think its very vital. Thank you maam. Is the u. S. Properly equipped to remain competitive in the Financial Services work force . This question is to dr. Lopez deprado and to ms. Fender. The u. S. Is the leader in the Financial Services today. My concern is that this leadership is being challenged by the fact that on one hand we are not investing as much in ai as other countries. And 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 but they will not be allowed to stay. And as a result, yes, we are very competitive and this competition this ability to train these skills is going to turn against us if we arent able to retain this talent. Okay. Ms. Fender. We have seen that, again, its 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. Theyre doing innovation hubs. They realize that this is something they need to be proactive about. We are seeing out of our case studies we had a criteria that things in our case study had to be in the practice. Theres a lot of talk out there but things that are in practice, five of the nine are here in the u. S. 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. So, when we think about underrepresentation of certain individuals and members of the work force who are not getting the types of education that are needed for the jobs that may be coming online as a result of automation in ai development. So, i think if we 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 and recognized for five minutes. I appreciate each of you being here today and i appreciate the chairman for holding this hearing. This is an important topic, something ive been passionate about since arriving here in congress. Dr. Lopez de prado, i appreciate your comments because what you have touched on is something ive been an ardent believer in for a long time. 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 that 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 to 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 necessary in the 21st century workplace but also ensuring they have the opportunity to get the retraining to continue competitiveness. As we see further growth and development in ai, it will require frequent training to stay relevant. That is a very competitive field, right . The second thing you touched on is something im even more ardent about is we educate a lot of kids in this country. We do Higher Education in this country better than anywhere else in the world. We invest a lot in those kids and politely ask them to leave at the end of their tenure here. That is embarrassing. That z idiotic. That is stupid and i hate that. I want to find a way to attract talent into this country and retain talent in this country. I believe this country can provide a crucible for technological development. I think technology will benefit human kind over all the world in the long run and i want to make sure you do that. I really appreciate you touching on those topics. Ms. Wegner, i know you have a source code event today, yesterday, tomorrow . This afternoon. This afternoon to talk about source code again and i appreciate you continuing to educate a lot of people about where that is. Where i go all the way across the district in indiana, i hear about how much technology, investment, ip is in things that arent readily seen in the source code, in the technology underpinning automation itself. So, i know how important that is and i really appreciate you bringing that to light. All that being said, i wanted to ask ms. Rejsjo a question that is maybe far afield from what were talking about today. I had people in my office earlier this week that were very complimentary of nasdaq surveillance services, they were Public Companies and how when something seems amiss in the markets nasdaq was quick to pick up the phone and Say Something seems amiss, lets figure out whats going on here. One of the things thats very important back home is biotech. A lot of biotech firms are based in the idea people dont know about that. Were trying to get the word out about it. Theyre concerned about market manipulation specifically with regard to short selling. Theyre promoting the idea there should be more disclosure around short selling similar to longings potentials. They came in 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 they made was nasdaq seems to be doing a good job of figuring out when theres potential manipulation. I wonder if you might touch on that. Is disclosure in short selling something that would benefit the market, something that would benefit these firms or 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 wonder if you might comment on that in the last minute. I think disclosure is a big part of surveillance, right . Yep. Its information is 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. Right, of course. So, its really to detect what is then being how its used in an unnormal way or a manipulative so you feel like you could detect the activity that would be illegal, abnormal, or different adequately. The question is what do we do it after that point is where we should focus Public Policy attention. Is that fair . Yes, but to be fair also, there are other parts in nasdaq that has more of the policy questions. Okay. But for me as a surveillance protectioner, i think the surveillance we have and the tools we have to monitor the markets are adequate. I think thats an important question because thats the question, where do we need to focus attention and its beyond surveillance and the penalties. With that, i yield back. Thank you. And im very encouraged that one of the areas of bipartisan agreement here is the insanity of this business of warning people their phd diplomas and pushing them back on an airplane. Thats one of the reasons i was proud to introduce this, the act of 2019 designed as a rifle shot to just exactly solve this problem. I really look forward to my colleagues support on this. Now i would like to recognize the gentlemanwoman from texas, ms. Garcia for five mines. Thank you mr. Chairman and thank you again for holding this hearing. Thank you to all the witnesses, good morning and welcome. And i wanted to focus on a couple of issues that some of you have already talked about. Like ms. Adams, i am particularly concerned about jobs. My district is from houston and 77 latino and working class. Were always concerned about jobs. Im encouraged that you all seem to have the consensus that there will be job displacement, that there will be new jobs created. And my main concern of course is whether or not we do have the skill sets, mr. Mcilwain, 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 ai and automation and how it can Work Together specifically in the area of Regulatory Compliance. So, ms. Fender, in your experience, has ai and automation affected institutions, Regulatory Compliance, is it improving . Is it still working progress or how are we doing . Thank you. Thats a very good question, and again i think its about its 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 maybe of 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 s. E. C. I think in terms of reg fd and so forth. Okay. Can you simplify and ensure Regulatory Compliance with the federal agencies in charge of supervising the Capital Markets . Sure. I think as the data sets become more complex, i think its going to be vital that the regulators have the resources to have their own ai either independently of the companies or together with the companies through Public Private partnerships as the bad actors become more sophisticated. And were talking about global bad actors. We need a strong cop on the beat here in the u. S. And i think its very important that the private sector Work Together with regulators to ensure that they have those resources and that Congress Really explores the s. E. C. And cftc have the resources they need because the systems are becoming much more complex and reg tech is evolving but needs to keep up with the pace of that technology. Well, i think that is a big certain of this committee is those bad actors as youve described them. So, how can ai assist us with antiMoney Laundering compliance and suspicious activity reporting . Are we wellprepared for that . I know we did a codel to several countries and things are getting more and more sophisticated. Absolutely. 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 that we focus on this. And i would say the new head of innovation at finra 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. So, do you in your opinion, do you think that our regulators in our oversight entities are wellprepared 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 technologies. I think midas has been a positive example of the fcc using technology to surveil the markets. But i think this is a constantly evolving space as everyone here has noted. Weve got to just 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 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. So, disclosures are really important. So, you know, understanding your clients, understanding where the money comes from, and understanding what clients are really getting all kind of goes together. All right. Thank you. Thank you both of you and i yield back. Thank you. The gentleman from virginia, mr. Riggleman is recognized for five minutes. I want to thank all the witnesses for being here today. Im not showing any favoritism. I would particularly like to welcome ms. Fender, shes located in my district. The institute provides a host of resources for the Service Industry who are the most qualified and the highest codes of standard in the financial industry. I am honored to have such a distinguished group preside in the fifth district. Although ms. Fender is not a constituent herself, her organization employees many of them. Ill start with you ms. Fender. You probably know that was going to happen. Can you talk about how cfa is adapting charter to these ai and Machine Learning investments in the industry. Thank you very much. Pleased to be here representing virginia. The Cfa Institute is really the global standard for inevestment practitioners. The people who have our potential are the Portfolio Managers for your 401k. Theyre the chief Investment Officer at the public pension fund. Theyre 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. And this is a significant indication that we are seeing the market change and we need to prepare people. So, we have a group called Practice Analysis team. And 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. Yeah. And really is a reason i asked the question. My prior job, we talked about monopolization of data and things of that nature. When i worked for the office of secretary of defense, so we had about 40 people looking at this. We had to look at all data, multidomain, across stove pipes to see how it includes that data, aggregate the data, analyze and execute that databased on how we templated Human Behavior. Looking at rules, ill start with ms. Rejsjo. With had we did this we had multiple data sets of people ive never seen before. We had multiple data sets. We had data we had never sort of aggregated and combined with other data sets. We thought we had the right answer and we found out we didnt in trying to template Human Behavior analysis. Do you think thats something youre going to see more in the future is there wont be human in the loop or there will be roles to develop Machine Learning, do you think well see more and r many of that taking humans out of the loop k loog at analysis or fraud or anything of that nature . So, thank we are a long way from that at least. I think that for now the way that we do it is really to have the data that we have. For us its really the order that we already have and analyze. So, now were just applying a new technique to give us more better overview that is not that parameter driven. But for us still i really think that 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 going to be there for a while. Its interesting you say 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 head nods back there. Dr. Lopez de prado, you talked about there could be advantages to aaggregating as much data as we can. Thats thing ive been trying to wrap my arms around. My whole job was not competition. It was to monopolize the data. To use the competition to give the best solutions we could for first, second, and Third Order Effects for what happened to the specific part of the network. This is a tough question because to be this objective in 40 seconds is going to be probably ridiculous. But when youre looking at this, do you think and i know this is a tough question. Do you think that 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 across multiple sets. Do you think we will have that type of sharing for proprietary Solutions Based on algorithmic types of solutions. Do you think that will happen or we have to force that to happen when we monopolize that kind of data. Are you referring to sharing the technologies with private and Public Companies . Yes. When you look at the nasdaq 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 cfdc and the fcc. In particular in my remarks i mentioned the crowd sourcing of investigations, how companies or private participants could establish tournaments to help agencies identify market manipulators. Thank you very much and i yield back the balance of my time. Thank you, the gentleman from illinois is recognized for five minutes. Thank you mr. Chair and thank you all for coming. I had back in my prior life, i had a head of engineering who had a theory that ive yet to prove wrong. Every advance in Technology Gives more precision and less knowledge. That was a guy who started with slide rules where he had to know the magnitude of order of his answer and could never remember whether it was millions or billions. And of course in my lifetime weve gone from fold able maps to gps that can give me the exact latitude or longitude and i cant tell you if im north, south, east, or west of where i started. Ai has struck me as putting that acceleration on steroids. And 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 and 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 get lower trading costs, lower spreads. And thats all terrific. The question i have and a lot of you have also talked about bad actors and 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 to imagine the trading algorithm that is tracking a bunch of data and has figured out how to bet on another and making money. I can imagine the trading algorithm thats looking at changes in currency flows for illegal activity thats not in itself illegal but is arbitraging some spread that results from that. So, ms. Wegner, i wonder if you would comment on that tension between transparency and algorithmic robustness and what degree we need regulatory tools to stipulate where we sit on the continuum. Sure. I think transparency is absolutely vital. I think its also very vital that regulators and 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 could just clarify. First would you agree that the more transparent the algorithm, potentially the less powerful the algorithm. To the extent that the algorithm is not subject to intellectual Property Rights, that transparent si is absolutely vital. If were talking about intellectual Property Rights in the source code of the algorithm, thats proprietary information. But by transparency 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. Oh, got it. I could give you the algorithm, but you couldnt understand what its doing. Sure. 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 theyre basing their analysis on the existing data sets. I dont think were totally there yet but i think thats something we feed to explore. What is our answer to it . That is an interesting balance. So, question, this is for you but really for all the panel is 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 importan question the industry should get together on. Number one, how should we do that and number two, what degree do we need to coordinate internationally. If we do this, because the markets are interlinked, is this a u. S. Problem or International Problem . Anybody have thoughts on that . If i may, this is a very important distinction. Tends to be less reliable than Transparent Solutions in particular in finance because we are dealing with problems where the signal to noise ratio is low. For instance in astrophysics research, why is the signal to noise ratio low in finance world . Because of competition. Otherwise everybody would be able to extract profits from the market. When we deploy black Box Solutions in the market, the solutions can identify patterns that are real. And confound these patterns with actual signal leading to Investment Strategies that will fail. So, one solution would be for investor to understand very carefully when a product is based on a black box solution as opposed to a transpiring Machine Learning solution. Okay. Yield back. I would welcome any of your comments. If youve got follow up in writing, please share. 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, International Development and Monetary Policy is recognized for five minutes. Thank you mr. Chairman. Really appreciate you calling this hearing and we 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 these smart watches, mr. Spok had one. And a lot of attention is 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. And to be straight, im one of those. You know, im conflicted. I know we cant hold back the wind. And its inevitable that were going to see more and more of this in the future. And im not sure that 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 thats where im concentrating most of my interest. Dr. Mcilwain, 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. I share a little bit of your fear because what we know persists as technology changes, as technological advances are made is that some people typically the same groups of people are left out, left behind, are disadvantaged. So even as technology is unpredictable. Some of those exclusions are very much predictable. 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. When we look at our Technology Sector, those who are prepared to be part of that sector. Those who are currently working building the technologies of today and tomorrow are tremendously unrepresentative of our full democracy of all the citizens of our country. 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 can have will be built for what purpose. 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. What do you think we should do or any of you do right now if we we have to have young people interested in and committed to the future and ais inevitable part of it. What should they do next week . What should young people be doing . How should we direct young people right now who are scientifically gifted. What should we do . Promote responsible innovation. Our members support trying to get out there to the middle school students, a diverse population of people, get them interested in stem fields. I think there is a lot of opportunities for companies to partner with some of the schools in a geographically diverse part of the country and help fund that. Recruit now. Kids get interested from a young age. Weve got to get in there early and make sure kids see role models at those firms. Thank you very much. Thank you. And now i guess we have time for a brief second round of questions here. Weve had sort of two different narratives that have been going on here, one is the optimistic narrative of the i guess the tshaped skills or human intelligence paring, human augmented intelligence. And then the intermediate way of transfer learning where youd use one field of expertise and transfer that to another field thereby replacing multiple human machine pairings. One example was from the geniuses at goldman using satellite data to analyzing satellite imaging to predict cement pricing in the future. And then potentially using transfer learning so that knowledge could be transferred to Copper Mining or whatever else it was. On the other hand, theres an alternative narrative, you aggregate all the data you can and say, i want a general purpose learning, trading algorithm to look at all satellite data and look for all market correlations. That would detect the cement market, look at the parking lots of toys r us to predict they were going bankrupt because they didnt have much cars. This could be written to deploy tens of thousands of machine human pairings here. And obviously with much, much Smaller Labor input and need for humans. So which of these two narratives are going to end up winning and how is it going to net out for human participation . Anyone who wants to tackle that tar baby. I can start and say that one of the foundational concepts in investing is correlation is not necessarily causation. We have a lot of data, see the patterns, but you need a human to ask, whats the right question . I mentioned the example of going through the news stories with bloomberg. They said the question was to go through and not say, what do we think the author of this article wanted to get across . But what do we think people are hearing . There are a lot of nuances about how this is going to play out. Thats why again, having sort of a collective intelligence and diverse perspectives, is going to be important. Doctor, do you have a yeah. I think that the two narratives have some part of truth. I think in the shortterm, we have reasons to be worried in terms of the transfer of knowledge and the potential displacement that will occur as these technologies are more broadly deployed. But i think in the long term we have reasons to be optimistic. Because the next generation will be better prepared than our generation, previous generations. It is very important that we give access to education, equal access to education. Its very important that we encourage kids to learn how to program, participate in math and engineering classes, and that we form a flexible workforce, a workforce that in the future we dont know what these technologies will do in 20 years, that they are able to engage proactively. But is there a danger that this is going to squeeze all the profitability out of Financial Services . That if you had complete knowledge of everything and very efficient algorithms immediately trading on that knowledge, the 30 improvement on your retirement savings, all of that used to end up in the pockets of people with nice homes on oyster bay. And thats sort of the nature of things. It may be that when we get this much more efficient economy with the extensive deployment of ai, the total amount of money will continue to go down the same way High Frequency trading is suffering that. One view if i may, in fact having such perfect market is not necessarily bad for society. Meaning that the day that we go to our Financial Adviser and we receive the same treatment that weve seen when we go to the doctor, essentially, there is a protocol of this is what you need to invest in order to achieve your retirement goals, i think thats a good outcome. As we see greater efficiencies, robo advisers and others, more asset managers, will be able to deploy that to the masses. It raises a global competition question. Were not just talking about competition domestically. With are talking about internationally. We are not going to stop time all across the world. Other countries are innovating in ai. Its inevitable. Well compete in that space and we want to keep the u. S. Markets the envy of the world. So if the future of Financial Advising is conversations with alexa, i guess it comes down to, you know, is the objective that a function that the ai running alexa is maximizing, is that amazons profit . Or is it some linear combination of amazons profit and diversity, inclusion, a secure retirement rather than steering people into products that are profitable for amazon. I think the vital part, we have competition, theres not too much aggregation of power in one entity. We need to have policies that promote robust competition amongst the robo advisers to make sure there is data accessible at competitive prices, not a barrier to entry. This is going to be an exciting space where financial meets judiciary, meets commerce committee. Were all finance is becoming more technology and technology more finance. These are the right questions. Thank you. And ill yield five minutes to the Ranking Member. Thank you, whether chairman. Miss horatio, id like to go back and continue our conversation that we were talking about. Cybersecurity and using ai and fraud and i wasnt very well managing my time before. So could you explain further how nasdaq is using ai and fraud detection . Yes. I think its important just to start that the future is here. Right . We have billions of data points. Its a massive amount of data that needs to be analyzed to capture anything that is then fraudulent or manipulative in the market. We have that environment already. What we have been doing so far is deploying algorithmic coding to be able to process all of this data very fast. Our realtime surveillance is picking up on unusual behavior within seconds after it has happened in the market. There is a fast and efficient way. But as it is growing and exponentially growing, there is the need to continue to invest in other ways of looking at it. Where ai then comes in. Its more a broader approach, and it doesnt have to be so parameter specific today so we can capture more things that are more sophisticated. As we have been talking about, its not only us using this technique. The participants in the market are using it as well. I think its important for us to match their technology with ours when we look at the types in the market or the behavior. Thank you. Doctor, can you touch on the differences between automated trading with algorithmic, High Frequency, and computer, how theyre not the same and what differentiates them Algorithmic Trading consists in following rules, computer follows some rules to achieve an outcome. It does not require Machine Learning. Machine learning is the learning of patterns from a set of data with us directing that learning. Essentially what happens is you give to an algorithm a data set, and the data set identifies a pattern that we were not aware of. Thats Machine Learning. What was the third . The automated trading . And High Frequency. It can happen with or without Machine Learning. In the earliest stages 2005 High Frequency trading occurred without intelligence. But today liquidity providers, hedge funds, deploy these with Machine Learning embedded. Thank you. Miss wegner, weve had some discussion on the cost savings that have resulted from ai and automation in the Capital Markets. Do you see that these efficiencies are a significant reason behind the record returns investors have enjoyed in the last decades . This has contributed. Every reduced cost of trading adds up with compounding over time. As the markets become more efficient, investors are going to have more. Whether theyre like half of americans invested in a 529 plan or otherwise, its been a net positive. I have no further questions. I yield back. Thank you. And the gentleman from missouri is now recognized for five minutes. Thank you, mr. Chairman. Im interested in, you know, how do we do planning now for the future . For example, we just updated our antiMoney Laundering bill or the bank secrecy act. And im sitting here now, and i introduced a bill, so ive been feeling pretty good about myself until you guys came up today. And im thinking, why did we go through all of that . You know, because the bad guys are out, you know, trying to figure out how they can, you know, exploit whatever we pass legislatively. How do you see ai involved in antiMoney Laundering efforts like the legislation that we hope to send will take up during our lifetime . Is there anyway you think that that can play a role, that ai can play a role in Money Laundering bills, how we are trying to reduce it . We are never going to probably eliminate it. Well, this is a gargantuan problem. We have to tackle tremendous amounts of data, and identify this needing in the haystack. I think a practical solution is for regulators to Work Together with data scientists, with the entire community, and crowd source these problems. We need to analyze this data and give it to the community so the Community Helps us enforce the law. Of course they can be rewarded with some of the fines levied against wrongdoers. But i think thats a very doable approach, given number one how difficult it would be for the agencies to develop the techniques that the wrongdoers are developing, and number two the amounts of data that we need to parse through. We had the treasury secretary before our committee yesterday, and of course i didnt even raise this issue. We had an agency, finsen, which is an investigatory part of the department of treasury. And so im here wondering what theyre doing, trying to keep up with the technology and what challenges theyre going to face in the future. And so, you know, you guys have destroyed almost everything i was proud of. But we appreciate you coming here anyway. Thank you very kindly. I yield back, mr. Chair. Thank you. And id like to thank our witnesses for their testimony today. Without objection all members will have five legislative days to submit written questions for the witnesses to the chair, who will forward them to the witnesses for their response. I would like to ask our witnesses to please respond as promptly as able. Theyll have five days to submit extraneous materials to the chair for inclusion in the record. This hearing is now adjourned. 0. Sunday night at 8 00 eastern American History tv on cspan trial looks back at the impeachment trial of bill clinton which took place over five weeks in january he and february of 1999. Were here today because the president suffered a terrible moral lapse. Infidelity. Not a breach against society. But it was a breach of his marriage vows. It was a breach of his family trust. It is a sex scandal. Exploring our nations past. Watch the clinton impeachment trial, sunday night at 8 00 eastern on American History tv on cspan 3. The house will be in order. For 40 years cspan has been providing america unfiltered coverage of congress, the white house, the supreme court, and Public Policy events from washington, d. C. And around the country. So you can make up your own mind created by cable in 1979. Cspan is brought to you by your local cable or satellite provider. Cspan, your unfiltered view of government. Join us monday for more from campaign 2020 with democratic president ial candidate tom steyer. Hes scheduled to speak at a breakfast in manchester, new hampshire. Live at 8 30 a. M. Eastern over on cspan 2, you can also watch online at cspan. Org or listen live on the free cspan radio app. Next a discussion about russia, nato and the European Union with the Ukrainian Deputy prime minister. Hosted by the German Marshal Fund earlier this month. This is an hour. Please grab a seat, we were going to