Master's in Financial Technology & Artificial Intelligence Curriculum

Core Competencies You Will Master

The MS in FinTech and AI program equips you to lead tech-first financial services businesses. You will gain a sophisticated blend of deep financial domain knowledge and mastery over evolving tech tools. You will learn Machine Learning, Artificial Intelligence, Large Data modeling, Data inference techniques, understanding and building AI agents, Large Language Models, and Computer Vision.

Through this curriculum, you will learn to navigate the changes that machine learning, digital currencies, and platform strategies bring to modern finance.

Advanced Domain Knowledge: Deepen your expertise in traditional and digital financial services.
Enabling Technologies: Master the tech tools and foundational concepts driving industry evolution.
Technological Transformation: Analyze and manage the structural shifts impacting critical financial functions.
Strategic Leadership: Gain the insights needed to spearhead growth and lead tech-first enterprises

Requirements for the Master of Science Degree in Financial Technology and Artificial Intelligence

The Master of Science degree in Financial Technology and Artificial Intelligence is available only under Plan B (non-thesis). A total of 45 credits are required for the degree.

1. All of the following pre-core courses (10.5 credits):

CSE 801C Introduction to Python Programming

3 Credits

Programming using Python. Design, implementation and testing of programs to solve problems such as those in engineering, mathematics and science. Programming fundamentals, functions, objects, and use of libraries of functions.

View full course description at the Office of the Registrar.
FI 801 Managerial Finance

3 Credits

Short-, intermediate- and long-term problems in Finance. Financial planning and control. Applications in domestic and international settings.

View full course description at the Office of the Registrar.
FI 891 Topics in Finance

1.5 Credits

Current and emerging issues in corporate finance, investments, and financial modeling to supplement and enrich existing courses.

View full course description at the Office of the Registrar.
MTH 800 Quantitative Foundations for Machine Learning

3 Credits

Advanced calculus and linear algebra tools for machine learning algorithms.

View full course description at the Office of the Registrar.

2. All of the following core courses (22.5 credits):

CSE 801A Introduction to Big Data Analysis

3 Credits

Introduction to data retrieval, processing, analysis, and visualization.

View full course description at the Office of the Registrar.
CSE 801 B Introduction to Data Mining

3 Credits

Basic techniques and algorithms for knowledge discovery in databases.

View full course description at the Office of the Registrar.
CSE 840 Computational Foundations in Artificial Intelligence

3 Credits

Conduct research in machine learning, artificial intelligence, deep learning, data mining, and other related fields.

View full course description at the Office of the Registrar.
FI 422 Financial Data Analytics (or FI872 see below)

3 Credits

Analysis of real-world financial data in a variety of settings. Apply textual analysis to large documents, identifying “sentiment” in search data, and back-testing trading strategies. Develop programming skills to collect and prepare data for analysis.

View full course description at the Office of the Registrar.
FI 872 Financial Data Analytics (or FI422 see above)

3 Credits

Introduction to the analysis of real-world financial data in a variety of settings. Applying textual analysis to large documents, identifying “sentiment” in Google search data, and back-testing trading strategies. Developing the programming skills necessary to both collect and prepare data for analysis. Identifying, downloading, cleaning, and shaping data.

View full course description at the Office of the Registrar.
FI 492 Additional Topics in Finance

1.5 Credits

Current and emerging issues in finance to supplement and enrich existing courses.

View full course description at the Office of the Registrar.
FI 824 Deep Learning and Neural Networks in Finance

3 Credits

Basic concepts in deep learning and neural networks in finance and economics. Practical experience implementing deep learning methods with state-of-the-art algorithms in a variety of machine learning packages with applications such as forecasting, algorithmic trading, and fraud detection.

View full course description at the Office of the Registrar.
FI 826 Financial Technology

3 Credits

Navigating the ongoing disruption of financial services and exploring technology-driven solutions designed to enhance traditional financial practices. Discovering foundational concepts of FinTech and how technology, coupled with regulatory and market changes, has revolutionized traditional financial services.

View full course description at the Office of the Registrar.
FI 844 Corporate Financial Strategies

3 Credits

Applying financial strategies to managerial decision making.

View full course description at the Office of the Registrar.

3. Complete 12 credits from an approved list of elective courses from the Finance (6 credits) and Computer Science and Engineering (6 credits) Departments. Other courses may be used to fulfill this requirement with approval of the program director.

Target Employers

Our curriculum directly aligns with the hiring needs of organizations looking to lead the future of digital finance:

Incumbent Financial Institutions: Tier-1 commercial banks, investment firms, and traditional insurance carriers adapting to digital transformation.
Fintech Startups & Scaleups: Agile disruptors focusing on decentralized finance (DeFi), neobanking, digital currencies, and peer-to-peer lending platforms.
Enterprise Tech & Consulting Firms: Technology giants developing proprietary financial tools and top-tier consultancies implementing enterprise-level fintech infrastructure.