Hunt Insights · Finance
A new course at the Woody L. Hunt College of Business gives finance students hands-on experience building artificial intelligence tools and applying them to real financial problems.
Build AI Applications Students program tools rather than simply use existing platforms.
Work With Real Financial Data Projects span markets, banking, credit, real estate, and financial documents.
Develop AI Agents Students explore systems that retrieve, analyze, calculate, and automate.
The Big Picture
FIN 4317: ML and AI Applications in Finance combines finance, programming, machine learning, and emerging AI technologies. Students use Python to build applications that retrieve and analyze financial information, perform calculations, and automate tasks.
Why It Matters
The course prepares students to build AI applications, evaluate the results those systems produce, and recognize where human judgment remains essential. Students work with recent, real-world financial data across investment markets, banking, credit, and real estate.
Projects include predicting loan defaults and housing prices and analyzing financial news, earnings reports, and SEC filings. Some assignments also incorporate Texas and El Paso data.
What They’re Building
One project asks students to create an AI investment committee made up of specialized AI agents.
Diego Leal, Ph.D.
Assistant Professor of Economics and Finance
“Imagine having a team of AI agents working together, just like analysts at an investment firm. One examines a company’s financial performance, another analyzes market trends, and others evaluate financial news and economic conditions.”
Students feed the system financial news, analyst forecasts, earnings call transcripts, SEC filings, stock prices, financial statements, and economic indicators.
A portfolio manager agent then combines that information into an investment recommendation, while a risk management agent reviews the decision.
Financial Information
Students work with financial news, analyst forecasts, earnings call transcripts, SEC filings, stock prices, financial statements, and economic indicators.
Investment Recommendation
A portfolio manager agent combines information from across the system into an investment recommendation.
Risk Management
A separate risk management agent reviews the decision and the information behind it.
The exciting part is that students build this system themselves, learning how AI agents can collaborate to process vast amounts of information and perform tasks that would normally require a team of financial professionals.
Diego Leal, Ph.D.
Building Skills for Finance
The course also introduces students to machine learning, large language models, natural language processing, and AI agents and how those technologies can work together.
Leal sees AI agents and natural language processing as especially relevant for students preparing for careers in finance.
Finance is an industry that relies heavily on information, and large language models are making it possible to process and analyze enormous amounts of text much faster and at a scale that wasn’t practical before.
Leal
Financial institutions are already applying the technologies to investment research, financial document analysis, and internal processes. AI agents can extend those capabilities by combining multiple sources of information and tools to complete more complex assignments.
The Bottom Line
Leal says the value is in teaching students how the technology works and how to apply it to a financial problem.
“What excites me most is that students can now build some of these systems themselves,” he said. “Knowing how to apply these technologies to real financial problems gives students a valuable advantage as they enter the workforce.”
Source: www.utep.edu
