Program Overview

Financial Technology MS

The Financial Technology MS program is designed to provide students with a comprehensive understanding of the technical and financial aspects of fintech. The program requires a minimum of 30 credit hours and is tailored for students with an undergraduate degree in business or other degree that satisfies the FINANCE 6500 pre-requisites.

Admission Requirements

Students must meet the general admission requirements for the Graduate School.

Degree Requirements

The program consists of the following required courses:

• FINANCE 6500: Financial Management 1 (3 credits)
• FINANCE 6503: Computer Applications in Finance (3 credits)
• FINANCE 6521: Financial Forensics: The Science of Derivatives (3 credits)
• FINANCE 6570: Introduction to Fintech (3 credits)
• FINANCE 6572: Financial Data Analytics (3 credits)
• FINANCE 6574: Artificial Intelligence and Machine Learning in Finance (3 credits)
• FINANCE 6576: Blockchain: Applications in Finance (3 credits)
• FINANCE 6590: Seminar in Finance (3 credits) Electives (6 credits): Choose two of the following courses:
  • FINANCE 6520: Security Analysis
  • FINANCE 6540: Financial Institutions and Financial Markets
  • FINANCE 6542: Real Estate
  • FINANCE 6545: Venture Capital and Private Equity
  • FINANCE 6580: International Financial Management The FINANCE 6500 course may be waived depending on the candidate's previous educational experience. If so, the candidate will need to take an extra course from the elective course list.

Learning Outcomes

Upon graduation, students will be able to:

• Describe the technical aspects of blockchain technology and distinguish between different types of consensus mechanisms.
• Identify the characteristics of various digital assets and critically evaluate the use-case and value proposition of the assets with respect to both risk and return.
• Develop a viable business plan around a blockchain-based asset.
• Summarize the robo-advising options that are offered from the financial industry.
• Compare and contrast the differences between modern peer-to-peer (P2P) lending platforms and traditional lending.
• Explain the structure of a trading bot and design/develop an algorithm to implement a trading bot.
• Demonstrate a proficiency in data science within the context of financial data analysis, including utilizing various APIs to automate data retrieval, working with various data formats, performing essential data cleansing, and producing insightful data visualizations.
• Describe and implement use cases of AI/ML techniques in financial applications and interpret the inputs/outputs of the models.
• Communicate both financial and technical aspects of a fintech project.