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 ExchangeCommission 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