The Future of AI and Big Data: Three Concepts

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“We’re probably in the second or third innings.”

he is Andrew Loess Status report on the progress of Artificial Intelligence (AI), Big Data and Machine Learning applications in Finance.

Lou, professor of finance at the MIT Sloan School of Management, and Ajay Agarwal of the Rotman School of Management, University of Toronto Shared his vision at the inaugural CFA Institute Alpha Summit in May. In a conversation moderated by Mary ChildsIn this article, he focused on three key concepts that he hopes will shape the future of AI and big data.

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1. Prejudice

Lo said applying machine learning to areas such as consumer credit risk management was certainly the first inning. But the industry is now trying to use machine learning tools to better understand human behavior.

In that process, the big question is whether machine learning will outgrow all of our existing human biases. For his part, Agarwal doesn’t think so.

“If we were having this conversation a few years ago, the question of bias would not even have been raised,” he said. “Everyone was worried about training their models. Now that we’ve achieved usefulness in many applications, we’ve started worrying about things like bias.”

So where does the concern about bias come from?

“We train our models with different types of human data,” explained Agarwal. “So if there is bias in human data, not only does the AI ​​learn bias, but they can potentially amplify the bias if they think it will increase their ability to optimize or make better predictions effectively.”

But AI can also be used to reduce biases. Agarwal cited the University of Chicago Study In which researchers developed AI programs that not only simulate human judges’ bail decisions but more accurately predict flight risk.

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2. Economics and Wealth Distribution

There is no doubt that AI increases productivity. But will AI create an employment crisis by rendering human workers obsolete? In Agarwal’s view, people are worried because we do not know where the new jobs will come from, nor do we know whether those who lose their jobs later in their careers will be relegated to serve in these new positions. Will I be able to train or not?

Innovation happens so fast today that we don’t know whether retraining programs will be as effective as ever, even for young workers who actually have the time and bandwidth to participate.

The second issue is that of wealth distribution. Will the adoption of AI lead to greater concentration of wealth?

“I would say that almost every economist is attached to the view that this will definitely lead to economic growth, and the total addition of wealth to the society,” Agarwal said. “But there is a divide among economists in terms of what this means for distribution. Some of us are very worried about the distribution.”

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3. Regulations

According to Low, there is a lot of opportunity for new types of data in the financial sector.

“There is much more we need to understand about the financial ecosystem, especially how” [inputs] interact with each other over time in a stochastic environment,” he said. “Machine learning is able to use vast amounts of data to identify relationships that we were not currently aware of, so I believe you are going to see very rapid progress from all of these AI methods.” have been applied to very small data sets so far.”

Agarwal expressed a related concern: “In regulated industries such as finance, healthcare and transportation, many of them do not have constraint data. We are restricted from deploying them due to regulatory constraints.”

Lou agreed on the potential for rules to hinder progress.

“There’s a complex set of issues that we currently don’t really know how to regulate,” he said. “A good example is autonomous vehicles. Currently, laws are made so that if someone gets into an accident and hits another passenger or pedestrian, they will be responsible. But if the AI ​​is responsible for the death, then who is responsible ? Unless we address that aspect of regulation, we won’t be able to make the kind of progress that we could have made.”

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AI and Machine Learning for all

So how can finance professionals develop machine learning, big data and artificial intelligence skills?

“There are really, really useful courses you can take to really get up to speed in these areas,” Low said. “But doing so just requires a certain amount of time, effort, and interest.”

According to Low, the younger generation is in the best position in this regard. In fact, today’s youth are more confident in the machine-human relationship, Agarwal said, as they have more time to spend on computers, mobile devices etc.

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As Low explained at the beginning, we are still very much in the early innings when it comes to applying these new technologies to finance. There are high hopes that they will boost productivity and lead to greater profits mixed with the concentration of wealth and panic about the potential effects of employment.

Still, concerns about human biases increasing AI and big data adoption may be overstated, while underestimating the potential barriers posed by regulations.

Still, given the inevitable adoption of AI in finance and beyond, finance professionals can’t afford not to know about it.

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All posts are the views of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of the CFA Institute or the author’s employer.


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Larry Kao, CFA

Larry Cao, CFA, Senior Director of Industry Research, CFA Institute, conducts original research with a focus on investment industry trends and investment expertise. His current research interests include multi-asset strategies and fintech (including AI, big data and blockchain). He has led the development of popular publications such as Fintech 2017: China, Asia and Beyond, Fintech 2018: The Asia Pacific Edition, Multi-Asset Strategies: The Future of Investment Management and AI Pioneers in Investment Management. He is also a frequent speaker at industry conferences on these topics. During his time in Boston as graduate studies at Harvard and a visiting scholar at MIT, he also co-authored a research paper with Nobel laureate Franco Modigliani, published by the American Economic Association in the Journal of Economic Literature Was. Larry has over 20 years of experience in the investment industry. Prior to joining the CFA Institute, Larry worked at HSBC as Senior Manager for the Asia Pacific Region. He began his career as a USD fixed-income portfolio manager at the People’s Bank of China. He also worked for US asset manager Munder Capital Management, managing US and international equity portfolios, and Morningstar/Ibbotson Associates, managing multi-asset investment programs for clients of a global financial institution. Larry has been interviewed by a wide range of business media such as Bloomberg, CNN, The Financial Times, South China Morning Post and Wall Street Journal.



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