AI is raising the bar of expectations for college graduates seeking jobs in finance, and Bryant’s "AI Applications in Finance" course is continually being refined to ensure that student skills match or exceed what employers want, says Huan Kuang, Ph.D., an assistant professor of Finance who has been teaching the course since its inception in the 2025-26 academic year.
“Entry-level jobs could be heavily affected by AI, because a lot of the things we used to think humans were essential for can be done at a relatively sophisticated level by AI,” says Kuang.
As a result, he says, educators not only have to teach AI skills but raise students’ level of competence in finance.
“We have to basically prepare students, not just at the entry level, but to what a second-year or third-year employee would know,” he says.
“We have to basically prepare students, not just at the entry level, but to what a second-year or third-year employee would know."
For example, entry-level employees were once tasked with extracting key financial information from annual shareholder reports or conference calls from public traded companies. That job can now be assisted by AI agents, so junior associates might be asked instead to work with and manage AI agents to finish the task. They may also be expected to provide more in-depth analysis and draw insights from a broader range of information while using AI as an analytical tool.
“Today, you can use large language models to analyze financial disclosures and find the hidden information behind the text without advanced coding skills,” says Kuang. “It used to require a lot of resources and specialized analytical skills to produce that kind of information. Now, AI makes this type of analysis much more accessible. If you have information other people don't, that gives you an edge in the markets.”
Setting the Course
The 400-level "AI Application in Finance" course includes instruction on basic coding skills and interaction with AI models. Emphasis is placed on real-world applications of AI in finance, including AI-assisted programming, using large language models to analyze financial information, and developing AI-enabled applications and agents.
“We want to equip students with skills they can talk about in a job interview, use in an internship, and most importantly use in their career,” says Kuang. “The course is an opportunity to work with practitioners to see exactly how they use AI in the industry.”
Kuang says there are some essential AI skills that finance students need to succeed in today’s job market, including some technical foundation such as basic programming knowledge and an understanding of how AI tools work.”
“When you talk with employers about the projects you’ve done with AI, you can’t just say I asked ChatGPT and it gave me these answers. Because everybody can do that,” says Kuang.
Students are not expected to know how to write code from scratch, but do need to learn the structure, framework, and the logic they need to solve a problem, he emphasizes.
“A lot of the actual coding will be generated by AI, but students need to know how to translate a finance topic into a program task,” says Kuang. “They need to know how to communicate with AI to get the output they want, and how to evaluate the output.”
One classroom exercise is to use vibe coding to create a viable financial services tool, which Kuang says will be a tangible accomplishment students can point to when employers ask them about their AI skills or how they would solve a problem using AI.
Meeting Employer Expectations
“When we talk to our corporate partners, more and more companies are looking for students who have the basic intuition or background to say, ‘I know there's something that can be done more efficiently or at lower cost using AI, and I've tried something and I'm working to learn more,’” says Kuang.
“When we talk to our corporate partners, more and more companies are looking for students who have the basic intuition or background to say, ‘I know there's something that can be done more efficiently or at lower cost using AI, and I've tried something and I'm working to learn more.'"
If entry-level employees are, on some level, competing with AI for their jobs, the differentiator is the ability to demonstrate their value — “to show our judgment, to show our knowledge” — Kuang stresses.
“If we raise expectations, our students can make things with the help of AI and using what we learn in class will impress employers,” he says. “I want students to build something that goes beyond a classroom exercise and has practical value.”
Kuang uses the example of creating a retirement simulator to demonstrate the importance of balancing AI skills with financial education in the classroom.
“With the help of AI agents, anyone could build a retirement simulator with one line of prompt,” he says. “The differentiators are how you model things like expected returns, investment risk, and asset allocations. All of this represents finance domain knowledge, which you need to drive the product further.”