Program Curriculum

The MScFinTech program is offered in one-year full-time and two-year part-time study modes. Both full-time and part-time students are required to complete a total of 30 credits# in core courses and electives.

 

Core Courses (8 courses, 16 credits)

AI for FinTech (2 credits)
This course covers the basic theories of artificial intelligence and machine learning, and their applications to FinTech. Topics include natural language understanding and sentiment analysis using various deep learning architectures.
The course also covers basic natural language processing methods for applications such as event and anomaly, fraud, and fake news detection. The course also relates sentiment and affect analysis to stock market trading, market
monitoring, and compliance and regulatory related adverse events.
Blockchain (2 credits)
This course introduces the basic concepts and technologies of blockchain from an engineering perspective, such as the Bitcoin architecture, consensus protocol of Bitcoin, proof of work, Ethereum, Hyperledger, smart contracts, and the
blockchain applications. The course also covers the limitations of and possible improvements to the blockchain system.
Corporate Finance (2 credits)
This course covers the valuation of cash flow streams (PV of cash flow streams, annuities, and perpetuities), valuation of bonds, valuation of stocks using the dividend discount model, capital budgeting decisions (NPV, IRR, payback),
capital structure, limits on the use of debt (tradeoff models), estimation of the cost of debt and equity, WACC, and terminal value.
Data Analysis (2 credits)
This course covers the basic and advanced statistical approaches to data analysis and the application of these techniques in analyzing financial data with statistical packages, such as Python and R. The key topics are reading and
describing data, categorical data, time series data, correlation, nonparametric comparisons, ANOVA, multiple regressions, general linear models, and quantile regression models.
Financial Data Mining (2 credits)
In this course, students first learn the basic concepts and techniques of data mining, including data preprocessing, data cleaning, clustering, classification, and outlier detection. The students then learn how to apply these techniques to
financial data, such as via sentiment analysis and social network mining.
FinTech Regulations and Compliance (2 credits)
This course provides students with the frameworks, concepts, and background needed to understand the roles that regulation, compliance, and assurance play in the FinTech markets from the technology and business perspectives.
The course also examines the perspectives of government officials, investors, managers, and consumers in terms of how they benefit from, guide, and influence the evolution of regulations and the associated compliance activities.
Foundations of FinTech (2 credits)
This course aims to provide a foundational introduction to financial technologies. More specifically, this course covers various important financial technologies and innovations, including investment and financing technologies such
as P2P lending and crowdfunding, payment technologies such as mobile payments, wealth management technologies such as robo-advisors, blockchain technologies such as cryptocurrencies, and other technologies such as InsurTech and RegTech.
Investment Analysis (2 credits)
An introduction to the fundamental concepts of investment analysis. The first part of this course covers the risk and return tradeoff, portfolio diversification, and modern portfolio theory, including the capital asset pricing model and
arbitrage pricing theory. The second part covers the basic analytical tools used in analyzing fixed income securities. Topics include interest rates and yield curve mathematics, duration, and convexity.

 

Required Course (1 course, 2 credits)

School of Science
Mathematical Foundation of FinTech# 2 credits

 

 

Electives* (a selection of 14 credits)

School of Business and Management
Cryptocurrency, Blockchain, and their Business Applications  2 credits
Economics of Financial Technology 2 credits
Entrepreneurship and Innovation in FinTech 2 credits
FinTech and Big Data Financial Analytics 2 credits
FinTech: The Future of the Financial Industry 2 credits
Portfolio Management with FinTech Applications 2 credits
Statistics for Financial Analysis 2 credits
School of Engineering
Decision Analytics for FinTech 3 credits
Optimization in FinTech 3 credits
Technology and Analytics of Alternative Finance 3 credits
School of Science
Statistical Machine Learning 3 credits
Statistical Methods in Finance 3 credits
 

* The electives offered in a particular year will be announced at the beginning of each academic year. Students have to take at least ONE elective course from each of SBM, SENG and SSCI. The offering of elective courses is subject to availability.

# Subject to the decision of the Program Director, promising students who do not have a strong mathematics background may be required to take the required course - Mathematical Foundation of FinTech (2 credits) on top of the graduation requirement of 30 credits.