AI Applications In Business & Finance New

Artificial intelligence (AI) and machine learning (ML) are transforming the financial industry, changing how banks, investment firms, hedge funds, and financial technology companies analyze information, manage risk, construct portfolios, detect fraud, and make investment decisions. This course introduces students to the rapidly evolving intersection of artificial intelligence, machine learning, data science, and financial markets through a combination of conceptual foundations, real-world financial applications, and hands-on analysis.

Students begin by exploring the fundamentals of financial markets and the role of data in financial decision-making. They are introduced to stocks, bonds, market returns, risk and diversification, and the ways financial institutions use quantitative models to analyze markets. Building on these foundations, students examine how machine learning differs from traditional statistical modeling and how algorithms can identify patterns in large financial datasets that may be difficult to detect using conventional methods.

Through accessible examples and guided exercises, students explore major machine learning techniques including linear and logistic regression, decision trees, random forests, clustering, and introductory neural networks. Students learn how these techniques can be applied to problems such as predicting stock returns, classifying companies, forecasting market trends, measuring financial risk, identifying unusual transactions, and constructing investment strategies. Emphasis is placed not simply on building models, but on understanding why a model works, how its performance should be evaluated, and why financial predictions can fail.