The introduction of ChatGPT and other artificial intelligence tools has the potential to upend careers and industries around the globe, and finance is no exception. Increasingly, finance professionals need to know how to leverage machine learning, large language models, and other AI-based tools to solve problems and guide decision-making.
“When I talk to practitioners in the field, I get a sense of urgency,” says Stefan Nagel, the Fama Family Distinguished Service Professor of Finance. “Investment management firms are starting to use these tools, and they’re trying to find people who can help them. I advise finance students to learn as much as possible about AI and machine learning, because those skills will be in demand.”
Recently, Chicago Booth launched a new program that offers aspiring finance professionals the opportunity to do just that. Designed for recent college graduates with strong quantitative backgrounds, the Asness and Liew Master in Finance Program provides a broad finance education that also prepares students to make an impact at the intersection of AI and finance and stand out in a fast-moving job market.
“Investment management firms are starting to use these [AI] tools, and they’re trying to find people who can help them. I advise finance students to learn as much as possible about AI and machine learning, because those skills will be in demand.”
Finance experts largely agree that AI has already had a profound effect on the industry, and that its impact will only grow. A recent Gartner survey, for example, finds that 58 percent of finance professionals are using the technology—a 21 percent increase from just a year ago. And according to a recent study by Citi GPS, the use of AI could increase profits in the banking industry by as much as 9 percent over the next five years.
As Dacheng Xiu, professor of econometrics and statistics, puts it, “AI is reshaping every aspect of finance—from the development of trading strategies and personalized wealth management to financial planning and the automation of tasks like data entry and report generation.”
In particular, natural language processing is facilitating the analysis of vast amounts of unstructured data such as earnings reports, regulatory filings, and news articles. Traditionally, many finance professionals have struggled to leverage these vast amounts of information to guide investment decisions or predict future earnings.
“There’s a lot of in-depth, lengthy documentation that’s difficult, if not impossible, for humans to sift through,” Nagel explains. “But AI makes it possible to process these documents quickly and collect relevant data to inform decision-making. That’s just one example of how we can now access and interpret data that wasn’t available to us before.”
Making the most of this technology will depend on practitioners and scholars who can develop tools, processes, and approaches to tap into its vast potential. Booth’s finance faculty are at the forefront of those efforts.
For example, Michael Weber, associate professor of finance, and his coauthors developed a way to use OpenAI’s ChatGPT to find hidden information in corporate earnings call transcripts. Professor Xiu and his coauthors found that portfolio predictions based on LLM information outperformed those based on more traditional models. Other researchers including Ralph S. J. Koijen, the AQR Capital Management Distinguished Service Professor of Finance and a Fama Faculty Fellow, are exploring how modern AI tools that are usually applied to text and images can be applied to data that is unique to financial markets.
And the same faculty whose research is expanding the AI landscape are bringing their insights to class, giving MiF students a head start on the skills they will need to navigate an increasingly tech-focused industry.
“In addition to learning about the [AI] tools themselves, students need to acquire the communication skills to talk about those tools.”
Indeed, AI tools are integral to the Asness and Liew MiF Program curriculum at Booth. A new course, Machine Learning and Finance, which will be offered Spring Quarter 2025, will immerse students in the intricacies of machine learning, and established courses such as Advanced Investments incorporate AI in coursework and discussions.
“As students work with large datasets and write Python code in my course, modern AI tools enable them to write code more quickly and ask the language models to explain code to them,” says Koijen, who teaches Quantitative Portfolio Management. “Moreover, students read recent academic papers as part of the course, and the AI tools are useful to understand, summarize, and make connections across papers.”
Xiu, who teaches the MiF Program’s Financial Analytics course, says he fully embraces the use of generative AI tools in his MBA classes, encouraging students to incorporate the technology in their learning and problem-solving processes to create new insights. But in his MiF class, he raises the bar a little higher.
“We delve deeper into the methodological and technical aspects of AI and ML tools,” he notes. “The class provides a more rigorous treatment of the underlying models and algorithms.”
The curriculum combines this technology focus with an emphasis on communication skills. As Nagel notes, some finance practitioners struggle to bridge the gap between highly technical employees and stakeholders with limited understanding of the field. As a result, he says, there’s a growing need for people who can effectively explain how AI tools work and how they can create value for companies and investors.
“We made sure to include training on skills such as communicating effectively and making powerful presentations,” Nagel says. “Because in addition to learning about the tools themselves, students need to acquire the communication skills to talk about those tools.”
The MiF Program can be completed in 15 months of study, with four core courses and nine electives required.
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