Tue. Oct 19th, 2021

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

This is Andrew Lo’s report on the progress of artificial intelligence (AI), big data, and machine learning applications in finance.

Lo, a professor of finance at the MIT Sloan School of Management, and Ajay Agrawal, of the Rotman School of Management at the University of Toronto, shared their perspective at the inaugural CFA Institute Alpha summit in May. In a conversation moderated by Mary Childs, they focused on three main concepts that they hope will shape the future of AI and big data.

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1. Biases

He said the application of machine learning to areas such as consumer credit risk management was certainly the first entry. But now the industry is trying to use machine learning tools to better understand human behavior.

In this process, the big question is whether machine learning will end up amplifying all of our existing human biases. For his part, Agrawal does not believe it.

“If we had that conversation a couple of years ago, the question of bias wouldn’t even have arisen,” he said. “Everyone cared about training their models. Now that we’ve got the utility of various apps, we’ve started to worry about things like bias. “

So where does the concern for bias come from?

“We form our models from various types of human data,” Agrawal explained. “Therefore, if there is bias in human data, not only does AI learn bias, but it can amplify it if they believe this will increase their ability to optimize or make predictions effectively.”

But AI can also be used to minimize bias. Agrawal cited a study from the University of Chicago in which researchers developed artificial intelligence programs that not only mimicked human judges ’bail decisions, but also predicted flight risk more accurately.

AI Pioneers in Investment Management

2. Economy and distribution of wealth

Undoubtedly, AI increases productivity. But will AI cause an employment crisis by making human workers obsolete? In Agrawal’s view, people are worried because we don’t know where the new jobs will come from or whether those who lose their jobs later in their careers will be able to recycle to fill those new jobs.

Innovation is happening so fast today that we don’t know if recycling programs will be as effective as in the past, even for younger workers who have the time and bandwidth to really get involved.

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

“I would say that almost all economists are aligned with the vision that will definitely lead to economic growth and therefore an overall increase in wealth for society,” Agrawal said. “But there is a division among economists as to what this means for distribution. Some of us are very concerned about distribution.

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3. Regulations

According to Lo, in the financial sector there are many opportunities to obtain new types of data.

“There is much more we need to understand about the financial ecosystem, particularly how [inputs] they interact with each other over time in a stochastic environment, ”he said. “Machine learning is able to use large amounts of data to identify relationships we didn’t currently know about, so I think you’ll see much faster advances in all of these artificial intelligence methods that have been applied to data so far “.

Agrawal raised a related concern: “In regulated industries such as finance, healthcare and transportation, the barrier for many of them is not data. We are restricted to deploying them due to regulatory barriers.”

He agreed on the potential of regulations to impede progress.

“There’s a complex set of issues that we don’t currently know how to regulate,” he said. “A good example is autonomous vehicles. Currently, the laws are set so that if someone has an accident and kills another passenger or pedestrian, they are responsible. But if an AI is responsible for a death, well, who is responsible for it? Until and if we do not resolve this aspect of the regulation, we will not be able to make the progress we can. “

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AI and machine learning for everyone

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

“There are a lot of really useful courses you can do to catch up on these areas,” Lo said. “But it only takes a certain amount of time, effort and interest to do that.”

According to Lo, the younger generation is better positioned in this regard. In fact, today’s youth rely more on machine-human relationships, Agrawal said, because they have simply had more time to spend on computers, mobile devices, and so on.

Tile for the future of work in investment management: report 2021

As Lo explained at the beginning, we are still very much in the first steps when it comes to applying these new technologies to funding. There are many hopes that will increase productivity and generate greater profits mixed with concern about the possible ramifications of wealth concentration and employment.

However, concerns about AI and the adoption of massive data that amplify human biases may be exaggerated while the potential barriers to regulation may be underestimated.

However, given the inevitable adoption of AI in finance and beyond, finance professionals cannot afford not to know about it.

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

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Larry Cao, CFA

Larry Cao, CFA, senior director of industry research, CFA Institute, conducts original research with a focus on investment industry trends and investment experience. His current research interests include multi-asset and FinTech strategies (including AI, big data, and blockchain). He has led the development of such popular publications 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, pursuing graduate studies at Harvard and as a visiting scholar at MIT, he also co-authored a research paper with Nobel laureate Franco Modigliani that was published in the Journal of Economic Literature by the American Economic Association. Larry has over 20 years of experience in the investment industry. Prior to joining the CFA Institute, Larry worked at HSBC as a senior manager for the Asia-Pacific region. He began his career at the People’s Bank of China as a manager of fixed-income portfolios in USD. He also worked for U.S. asset managers Munder Capital Management, managing U.S. and international equity portfolios, and Morningstar / Ibbotson Associates, managing multi-asset investment programs for a clientele of global financial institutions. Larry has been interviewed by a wide range of corporate media, including Bloomberg, CNN, the Financial Times, South China Morning Post and the Wall Street Journal.

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