The MSFTA curriculum blends theory with practice. Students complete core coursework in statistics, machine learning, and financial modeling, followed by electives in specialized fintech areas.
Core Areas:
- Statistical Computing for Finance
- Machine Learning and AI for Financial Applications
- Financial Data Engineering
- Algorithmic Trading and Market Microstructure
- Risk Modeling and Quantitative Methods
- Blockchain and Digital Financial Systems
Electives:
Students may specialize in areas such as credit analytics, portfolio management, derivatives modeling, regtech, and crypto-finance.
The program requires 30 credits (10 courses), with 8 core/required courses (24 credits) and 2 elective courses (6 credits).
The program emphasizes hands-on learning and practical applications through courses, where students work on real-world problems. The curriculum provides ample experiential learning opportunities through projects embedded in courses. The diversity of electives enables students to explore advanced topics in areas like blockchain, machine learning, and ethical statistical learning, ensuring a rich and adaptable academic experience. While there is no formal thesis requirement, the program requires students to submit a technical paper on a FinTech-related topic, which provides a structured opportunity for students to showcase their expertise and analytical skills in FinTech and serves as a capstone to their academic experience. This requirement can be fulfilled through a paper derived from a course project, internship, or faculty-supervised research.